48 results on '"Jenny Y. Chien"'
Search Results
2. Optimization of trial duration to predict long‐term HbA1c change with therapy: A pharmacometrics simulation‐based evaluation
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Hanna Kunina, Alex Al‐Mashat, Jenny Y. Chien, Parag Garhyan, and Maria C. Kjellsson
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Therapeutics. Pharmacology ,RM1-950 - Abstract
Abstract Glycated hemoglobin (HbA1c) is the main biomarker of diabetes drug development. However, because of its delayed turnover, trial duration is rarely shorter than 12 weeks, and being able to predict long‐term HbA1c with precision using data from shorter studies would be beneficial. The feasibility of reducing study duration was therefore investigated in this study, assuming a model‐based analysis. The aim was to investigate the predictive performance of 24‐ and 52‐week extrapolations using data from up to 4, 6, 8 or 12 weeks, with six previously published pharmacometric models of HbA1c. Predictive performance was assessed through simulation‐based dose–response predictions and model averaging (MA) with two hypothetical drugs. Results were consistent across the methods of assessment, with MA supporting the results derived from the model‐based framework. The models using mean plasma glucose (MPG) or nonlinear fasting plasma glucose (FPG) effect, driving the HbA1c formation, showed good predictive performance despite a reduced study duration. The models, using the linear effect of FPG to drive the HbA1c formation, were sensitive to the limited amount of data in the shorter studies. The MA with bootstrap demonstrated strongly that a 4‐week study duration is insufficient for precise predictions of all models. Our findings suggest that if data are analyzed with a pharmacometric model with MPG or FPG with a nonlinear effect to drive HbA1c formation, a study duration of 8 weeks is sufficient with maintained accuracy and precision of dose–response predictions.
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- 2022
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3. PK/PD modeling links accelerated resolution of COVID‐19‐related clinical symptoms to SARS‐CoV‐2 viral load reduction in patients following treatment with Bamlanivimab alone or Bamlanivimab and Etesevimab together
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C. Steven Ernest II, Jenny Y. Chien, Dipak R. Patel, and Emmanuel Chigutsa
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Therapeutics. Pharmacology ,RM1-950 - Abstract
Abstract The relationship between severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2) viral load reduction and disease symptom resolution remains largely undefined for coronavirus disease 2019 (COVID‐19). While the vaccine‐derived immunity takes time to develop, neutralizing monoclonal antibodies offer immediate, passive immunity to patients with COVID‐19. Bamlanivimab and etesevimab are two potent neutralizing monoclonal antibodies directed to the receptor binding domain of the spike protein of SARS‐CoV‐2. This study aims to describe the relationship between viral load and resolution of eight common COVID‐19‐related symptoms in patients following treatment with neutralizing monoclonal antibodies (bamlanivimab alone or bamlanivimab and etesevimab together), in a phase II clinical trial. Corresponding pharmacokinetics (PKs), viral load, and COVID‐19‐related symptom data were modeled using Nonlinear Mixed Effects Modeling to describe the time‐course of eight COVID‐19‐related symptoms in an ordered categorical manner (none, mild, moderate, and severe), following administration of bamlanivimab or bamlanivimab and etesevimab together to participants with COVID‐19. The PK/pharmacodynamic (PD) models characterized the exposure‐viral load‐symptom time course of the eight preselected COVID‐19‐related symptoms. Baseline viral load (BVL), change in viral load from baseline, and time since the onset of symptoms, demonstrated statistically significant effects on symptom score probabilities. Higher BVL generally indicated an increased probability of symptom severity. The severity of symptoms decreased over time, partially driven by the decrease in viral load. The effect of increasing time resulting in decreased severity of symptoms was over and above the effect of decreasing viral load. Administration of bamlanivimab alone or together with etesevimab results in a faster time to resolution of COVID‐19‐related symptoms compared to placebo.
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- 2022
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4. A Quantitative Modeling and Simulation Framework to Support Candidate and Dose Selection of Anti‐SARS‐CoV‐2 Monoclonal Antibodies to Advance Bamlanivimab Into a First‐in‐Human Clinical Trial
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Matthew M. Riggs, Emmanuel Chigutsa, Ahmed Elmokadem, Jenny Y. Chien, Eric Burroughs Jordie, Ajay Nirula, and Tim Knab
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Physiologically based pharmacokinetic modelling ,medicine.drug_class ,In silico ,Dose-Response Relationship, Immunologic ,Pharmacology ,Antibodies, Monoclonal, Humanized ,Monoclonal antibody ,Antiviral Agents ,Models, Biological ,Article ,Therapeutic index ,Pharmacokinetics ,Humans ,Medicine ,Computer Simulation ,Pharmacology (medical) ,Clinical Trials as Topic ,Dose-Response Relationship, Drug ,SARS-CoV-2 ,business.industry ,Research ,Therapeutic effect ,Antibodies, Monoclonal ,Antibodies, Neutralizing ,Clinical trial ,Tolerability ,business - Abstract
Neutralizing monoclonal antibodies (mAb), novel therapeutics for the treatment of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), have been urgently researched from the start of the pandemic. The selection of the optimal mAb candidate and therapeutic dose were expedited using open-access in silico models. The maximally effective therapeutic mAb dose was determined through two approaches; both expanded on innovative, open-science initiatives. A physiologically-based pharmacokinetic (PBPK) model, incorporating physicochemical properties predictive of mAb clearance and tissue distribution, was used to estimate mAb exposure that maintained concentrations above 90% inhibitory concentration of in vitro neutralization in lung tissue for up to 4 weeks in 90% of patients. To achieve fastest viral clearance following onset of symptoms, a longitudinal SARS-CoV-2 viral dynamic model was applied to estimate viral clearance as a function of drug concentration and dose. The PBPK model-based approach suggested that a clinical dose between 175 and 500 mg of bamlanivimab would maintain target mAb concentrations in the lung tissue over 28 days in 90% of patients. The viral dynamic model suggested a 700 mg dose would achieve maximum viral elimination. Taken together, the first-in-human trial (NCT04411628) conservatively proceeded with a starting therapeutic dose of 700 mg and escalated to higher doses to evaluate the upper limit of safety and tolerability. Availability of open-access codes and application of novel in silico model-based approaches supported the selection of bamlanivimab and identified the lowest dose evaluated in this study that was expected to result in the maximum therapeutic effect before the first-in-human clinical trial.
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- 2021
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5. First‐in‐Human Study of Bamlanivimab in a Randomized Trial of Hospitalized Patients With COVID‐19
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Charles Benson, Emmanuel Chigutsa, Amit Saxena, Paul Klekotka, Peter Chen, Gourab Datta, Ariel Kay, Josh Poorbaugh, Mark J. Mulligan, William A. Fischer, Ying Grace Li, Karen L. Price, Patricia Brown-Augsburger, Ajay Nirula, Robert J. Benschop, Nadine Rouphael, Jenny Y. Chien, Jeffrey Fill, and Michael Dougan
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Adult ,Male ,medicine.medical_specialty ,Placebo ,Antibodies, Monoclonal, Humanized ,Antiviral Agents ,Article ,law.invention ,Pharmacokinetics ,Randomized controlled trial ,Double-Blind Method ,law ,Internal medicine ,Medicine ,Humans ,Pharmacology (medical) ,Adverse effect ,Fatigue ,Aged ,Pharmacology ,Dose-Response Relationship, Drug ,business.industry ,Research ,Headache ,COVID-19 ,Articles ,Middle Aged ,COVID-19 Drug Treatment ,Clinical trial ,Hospitalization ,Tolerability ,Pharmacodynamics ,Monoclonal ,Administration, Intravenous ,Female ,business - Abstract
Therapeutics for patients hospitalized with coronavirus disease 2019 (COVID-19) are urgently needed during the pandemic. Bamlanivimab is a potent neutralizing monoclonal antibody that blocks severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) attachment and entry into human cells, which could potentially lead to therapeutic benefit. J2W-MC-PYAA was a randomized, double-blind, sponsor unblinded, placebo-controlled, single ascending dose first-in-human trial (NCT04411628) in hospitalized patients with COVID-19. A total of 24 patients received either placebo or a single dose of bamlanivimab (700 mg, 2,800 mg, or 7,000 mg). The primary objective was assessment of safety and tolerability, including adverse events and serious adverse events, with secondary objectives of pharmacokinetic (PK) and pharmacodynamic analyses. Treatment-emergent adverse event (TEAE) rates were identical in the placebo and pooled bamlanivimab groups (66.7%). There were no apparent dose-related increases in the number or severity of TEAEs. There were no serious adverse events or deaths during the study, and no discontinuations due to adverse events. PKs of bamlanivimab is linear and exposure increased proportionally with dose following single i.v. administration. The half-life was ~ 17 days. These results demonstrate the favorable safety profile of bamlanivimab, and provided the initial critical evaluation of safety, tolerability, and PKs in support of the development of bamlanivimab in several ongoing clinical trials.
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- 2021
6. Glycemic Control with Once Weekly Basal Insulin Fc (BIF) in Persons with Type 2 Diabetes Mellitus (T2DM) Using Continuous Glucose Monitoring (CGM) in a Phase 2 Study
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Christof Kazda, Jenny Y Chien, Qianyi Zhang, Emmanuel Chigutsa, William H Landschulz, Paula K Wullenweber, Axel Haupt, Juan Frias, and Thomas Forst
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- 2022
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7. Development and Verification of a Body Weight–Directed Disease Trial Model for Glucose Homeostasis
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Nashid Farhan, Karthik Lingineni, Parag Garhyan, Jenny Y. Chien, Xiaosu Ma, Katharina Wieser, Ines Gebert, Stephan Schmidt, and Yifan Xing
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Blood Glucose ,Disease ,Type 2 diabetes ,Bioinformatics ,Models, Biological ,030226 pharmacology & pharmacy ,03 medical and health sciences ,0302 clinical medicine ,Weight loss ,Weight Loss ,medicine ,Humans ,Insulin ,Glucose homeostasis ,Pharmacology (medical) ,Glycated Hemoglobin ,Pharmacology ,business.industry ,Linear model ,Type 2 Diabetes Mellitus ,medicine.disease ,Obesity ,Clinical trial ,Diabetes Mellitus, Type 2 ,030220 oncology & carcinogenesis ,Insulin Resistance ,medicine.symptom ,business ,Biomarkers - Abstract
Weight loss has been associated with improvement in insulin sensitivity. It is consequently a cornerstone in the management of type 2 diabetes mellitus (T2DM). However, the strictly quantitative relationship between weight loss, insulin sensitivity, and clinically relevant glucose homeostasis biomarkers as well as changes therein as T2DM progresses is not yet fully understood. Therefore, the objective of our research was to establish a body weight-directed disease trial model for glucose homeostasis. To that end, we conducted a model-based meta-analysis using time course data of body weight loss (following lifestyle change or surgical procedure) and corresponding improvement of insulin sensitivity expressed as the Matsuda index. Changes in body weight were best described by a sigmoidal Emax model, whereas changes in the Matsuda index were best described by a linear model with a slope of 3.49. Once developed and verified, the model-based meta-analysis was linked to a disease-drug trial model for T2DM previously developed by our group to characterize and predict the impact of weight loss on clinically relevant glucose homeostasis biomarkers. The joint model was then used to conduct clinical trial simulations, which showed that weight loss can greatly improve clinically relevant glucose homeostasis biomarkers in T2DM patients.
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- 2020
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8. 192-OR: Glycemic Control with Once-Weekly Basal Insulin Fc (BIF) in Persons with Type 2 Diabetes Mellitus (T2DM) Using Continuous Glucose Monitoring (CGM) in a Phase 2 Study
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Paula Wullenweber, William H. Landschulz, Qianyi Zhang, Christof M. Kazda, Juan P. Frias, Axel Haupt, Emmanuel Chigutsa, and Jenny Y. Chien
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medicine.medical_specialty ,business.industry ,Endocrinology, Diabetes and Metabolism ,Basal insulin ,Insulin ,medicine.medical_treatment ,Once weekly ,Type 2 Diabetes Mellitus ,Phases of clinical research ,Hypoglycemia ,medicine.disease ,Diabetes mellitus ,Internal medicine ,Internal Medicine ,medicine ,business ,Glycemic - Abstract
Basal insulin Fc (BIF; LY3209590) is a novel, once-weekly, long-acting IgG Fc-fusion protein assessed for the treatment of diabetes mellitus. A 32-week study evaluating the safety and efficacy of BIF vs. degludec in persons with T2DM previously treated with a basal insulin showed HbA1c non-inferiority of BIF vs. degludec with significantly fewer hypoglycemic events (≤70 mg/dL). Here we present CGM data derived by Dexcom G6, allowing a more detailed assessment of glycemic control of BIF vs. degludec. The study included 2 dosing algorithms for BIF with different fasting glucose (FG) targets: ≤140 mg/dL (BIF-A1) and ≤120 mg/dL (BIF-A2). Degludec was titrated to FG ≤100 mg/dL. Subjects were randomized to 1 of the 3 arms. Subject (N=399) mean age was 60.2 yrs and baseline HbA1c was 8.1%. For the entire 32 weeks, the percent of 24 hrs in range, hyperglycemia and hypoglycemia was similar for the 3 arms (Figure). At Week 32, total duration of hypoglycemia was similar across 7 days post-injection for BIF-A1 and A2, showing that duration of hypoglycemia is independent of day post-injection. CGM data confirm that BIF showed similar glycemic control vs. degludec despite higher FG targets and numerically lower time in hypoglycemia. The flat pharmacokinetic profile enables near peakless insulin concentrations without an increase in hypoglycemia risk at highest exposure. Disclosure C. M. Kazda: Employee; Self; Eli Lilly and Company. J. Chien: Stock/Shareholder; Self; Eli Lilly and Company. Q. Zhang: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company. E. Chigutsa: Employee; Self; Eli Lilly and Company. W. Landschulz: Employee; Self; Lilly Diabetes, Employee; Spouse/Partner; Covance Inc., Stock/Shareholder; Self; Lilly Diabetes. P. Wullenweber: None. A. Haupt: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company. J. P. Frias: Consultant; Self; 89bio, Inc., Altimmune, Axcella Health Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Gilead Sciences, Inc., Intercept Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk, Pfizer Inc., Sanofi, Research Support; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Bristol-Myers Squibb Company, CymaBay Therapeutics, Eli Lilly and Company, Intercept Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc., Madrigal Pharmaceuticals, Inc., Merck & Co., Inc., Novartis Pharmaceuticals Corporation, Novo Nordisk, Pfizer Inc., Sanofi, Speaker’s Bureau; Self; Merck & Co., Inc., Sanofi. Funding Eli Lilly and Company
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- 2021
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9. Development and Qualification of a Drug-Disease Modeling Platform to Characterize Clinically Relevant Endpoints in Type 2 Diabetes Trials
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Puneet Gaitonde, Parag Garhyan, Stephan Schmidt, Felipe K. Hurtado, and Jenny Y. Chien
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Adult ,Blood Glucose ,Male ,Oncology ,medicine.medical_specialty ,Time Factors ,Combination therapy ,Endpoint Determination ,medicine.drug_class ,030209 endocrinology & metabolism ,Type 2 diabetes ,Models, Biological ,030226 pharmacology & pharmacy ,03 medical and health sciences ,0302 clinical medicine ,Diabetes mellitus ,Internal medicine ,medicine ,Humans ,Hypoglycemic Agents ,Insulin ,Pharmacology (medical) ,Thiazolidinedione ,Aged ,Glycemic ,Aged, 80 and over ,Glycated Hemoglobin ,Pharmacology ,Clinical Trials as Topic ,Dose-Response Relationship, Drug ,business.industry ,nutritional and metabolic diseases ,Type 2 Diabetes Mellitus ,Middle Aged ,medicine.disease ,Metformin ,Clinical trial ,Sulfonylurea Compounds ,Treatment Outcome ,Diabetes Mellitus, Type 2 ,Research Design ,Drug Therapy, Combination ,Female ,Thiazolidinediones ,business ,Biomarkers ,medicine.drug - Abstract
Type 2 diabetes mellitus (T2DM) is a chronic, progressive disease characterized by persistently elevated blood glucose concentration (hyperglycemia). We developed a mechanistic drug-disease modeling platform based on data from more than 4,000 T2DM subjects in seven phase II/III clinical trials. The model integrates longitudinal changes in clinically relevant biomarkers of glycemic control with information on baseline disease state, demographics, disease progression, and different therapeutic interventions, either when given alone or as add-on combination therapy. The model was able to simultaneously characterize changes in fasting plasma glucose, fasting serum insulin, and glycated hemoglobin A1c following administration of sulfonylurea, metformin, and thiazolidinedione as well as disease progression in clinical trials ranging from 16-104 weeks of treatment. The mechanistic components of this generalized mechanism-based platform, based on knowledge of pharmacology, insulin-glucose homeostatic feedback, and diabetes pathophysiology, allows its application to be further expanded to other antidiabetic drug classes and combination therapies.
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- 2018
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10. Once Weekly Basal Insulin Fc (BIF) is Safe and Efficacious in Patients with Type 2 Diabetes Mellitus (T2DM) Previously Treated With Basal Insulin
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William H. Landschulz, Christof M. Kazda, Jenny Y. Chien, Emmanuel Chigutsa, Paula Wullenweber, Axel Haupt, Qianyi Zhang, and Juan P. Frias
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0301 basic medicine ,Insulin degludec ,medicine.medical_specialty ,Endocrinology, Diabetes and Metabolism ,medicine.medical_treatment ,030209 endocrinology & metabolism ,Improving Diabetes Care: Hospital Discharge, Complications, and Novel Insulin Therapy ,Gastroenterology ,03 medical and health sciences ,0302 clinical medicine ,Diabetes mellitus ,Internal medicine ,medicine ,Dosing ,Glycemic ,business.industry ,Insulin ,fungi ,Type 2 Diabetes Mellitus ,medicine.disease ,Diabetes Mellitus and Glucose Metabolism ,030104 developmental biology ,Tolerability ,medicine.symptom ,business ,Weight gain ,AcademicSubjects/MED00250 - Abstract
Basal insulin Fc (BIF; LY3209590) is a novel, once-weekly, long-acting IgG Fc-fusion protein that is being assessed for the treatment of diabetes mellitus. The presented study evaluated the safety and efficacy of BIF compared to insulin degludec over 32 weeks in patients with T2DM previously treated with oral antidiabetic drugs and a basal insulin. The study design included 2 different dosing algorithms for BIF (BIF-A1 and BIF-A2) with two different fasting glucose (FG) targets of ≤140 mg/dL (BIF-A1) and ≤120 mg/dL (BIF-A2). Insulin degludec was titrated to a FG target of ≤100mg/dL using a modified Riddle treat-to-target algorithm. Study participants (N=399) were randomized in a 1:1:1 ratio to 1 of 3 parallel treatment groups. The average age of participants was 60.2 years, baseline HbA1c was 8.1% and duration of diabetes 14.7 years. There were no statistically significant differences in demographics or baseline characteristics across the 3 treatment groups. Both BIF groups achieved non-inferiority (non-inferiority margin = 0.4%) for the primary endpoint of HbA1c change from baseline to Week 32 with a mean±SE reduction for BIF-A1, BIF-A2 and insulin degludec of 0.6±0.1%, 0.6±0.1% and 0.7±0.1%, respectively. In line with the different fasting serum glucose (FSG) targets, insulin degludec achieved greater FSG lowering from baseline as compared to the BIF arms. Similarly, both BIF dosing groups showed significantly fewer hypoglycemic events compared to insulin degludec (all documented events as well as nocturnal events) when assessing events ≤70 mg/dL (3.9 mmol/L). Hypoglycemic events
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- 2021
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11. Simulation-Based Evaluation of Dose-Titration Algorithms for Rapid-Acting Insulin in Subjects with Type 2 Diabetes Mellitus Inadequately Controlled on Basal Insulin and Oral Antihyperglycemic Medications
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Jenny Y. Chien, Xiaosu Ma, Jennal Johnson, James Malone, and Vikram Sinha
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medicine.medical_specialty ,endocrine system diseases ,Endocrinology, Diabetes and Metabolism ,medicine.medical_treatment ,Insulin Glargine ,030209 endocrinology & metabolism ,Type 2 diabetes ,030226 pharmacology & pharmacy ,03 medical and health sciences ,0302 clinical medicine ,Endocrinology ,Internal medicine ,Diabetes mellitus ,medicine ,Humans ,Hypoglycemic Agents ,Insulin lispro ,Computer Simulation ,Dosing ,Glycemic ,Glycated Hemoglobin ,Insulin Lispro ,Dose-Response Relationship, Drug ,business.industry ,Insulin ,digestive, oral, and skin physiology ,nutritional and metabolic diseases ,Type 2 Diabetes Mellitus ,Models, Theoretical ,medicine.disease ,Metformin ,Medical Laboratory Technology ,Diabetes Mellitus, Type 2 ,business ,Algorithm ,Algorithms ,hormones, hormone substitutes, and hormone antagonists ,medicine.drug - Abstract
The purpose of this prospective, model-based simulation approach was to evaluate the impact of various rapid-acting mealtime insulin dose-titration algorithms on glycemic control (hemoglobin A1c [HbA1c]).Seven stepwise, glucose-driven insulin dose-titration algorithms were evaluated with a model-based simulation approach by using insulin lispro. Pre-meal blood glucose readings were used to adjust insulin lispro doses. Two control dosing algorithms were included for comparison: no insulin lispro (basal insulin+metformin only) or insulin lispro with fixed doses without titration.Of the seven dosing algorithms assessed, daily adjustment of insulin lispro dose, when glucose targets were met at pre-breakfast, pre-lunch, and pre-dinner, sequentially, demonstrated greater HbA1c reduction at 24 weeks, compared with the other dosing algorithms. Hypoglycemic rates were comparable among the dosing algorithms except for higher rates with the insulin lispro fixed-dose scenario (no titration), as expected. The inferior HbA1c response for the "basal plus metformin only" arm supports the additional glycemic benefit with prandial insulin lispro.Our model-based simulations support a simplified dosing algorithm that does not include carbohydrate counting, but that includes glucose targets for daily dose adjustment to maintain glycemic control with a low risk of hypoglycemia.
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- 2017
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12. Basal Insulin Fc (BIF), A Novel Insulin Suited For Once Weekly Dosing For The Treatment of Patients With Diabetes Mellitus
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Julie S. Moyers, John Michael Beals, Jenny Y. Chien, Oliver Klein, Tim Heise, Edward J. Pratt, Axel Haupt, and Charles Benson
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medicine.medical_specialty ,business.industry ,Endocrinology, Diabetes and Metabolism ,Basal insulin ,Insulin ,medicine.medical_treatment ,Once weekly ,medicine.disease ,Endocrinology ,Internal medicine ,Diabetes mellitus ,medicine ,Dosing ,business - Abstract
An optimally designed once-weekly basal insulin with reduced day-to-day pharmacokinetic (PK)/pharmacodynamic (PD) fluctuations compared to daily basal insulins should have a low peak-to-trough ratio at steady state. An insulin with this flat profile could improve glycemic efficacy while reducing hypoglycemia. Basal insulin Fc (BIF; LY3209590) is an insulin IgG Fc-fusion protein developed for once weekly dosing. The results of the first in-human studies of BIF assessing the safety, tolerability, PK, and PD following single and once-weekly doses of BIF are presented below. The single ascending dose (SAD) study assessed 6 dose levels of BIF, administered to healthy subjects or patients with type 2 diabetes mellitus (T2DM). In the multiple ascending dose (MAD) study, patients with T2DM previously treated with basal insulin received a one-time loading dose at Week 1 followed by a once-weekly maintenance dose for 5 additional weeks. Four fixed-dose maintenance dose levels were evaluated. The loading dose was implemented to rapidly achieve steady-state BIF concentration at each dose level. Patients with T2DM in the control group received insulin glargine at the same dose as their previous daily insulin dose. Key objectives were safety and tolerability, PK endpoints with a focus on half-life and peak-to-trough ratio at steady state, and finally PD measures. The SAD study included 57 patients with T2DM and 16 healthy subjects. The mean age of patients with T2DM was 58.4 years and the mean BMI was 29.5±3.2 kg/m2. The mean age of healthy subjects was 35.8±9.3 years and the mean BMI was 26.1±3.1 kg/m2. In the SAD study, BIF demonstrated linear PK with dose-proportional concentration profiles in healthy subjects and patients with T2DM. The maximum BIF concentration was reached on Day 4. BIF had a mean half-life of approximately 17 days in patients with T2DM. Following a single dose of BIF, a decrease in FBG was observed on Day 1 and was sustained until at least 5 days post-dose. In the MAD study in 33 subjects with T2DM aged between 40 and 69 years, BIF demonstrated a nearly peak-less PK profile over a one-week dosing interval with a peak-to-trough ratio of ~1.1 at steady state. This flat profile is in contrast to insulin glargine. Following once-daily dosing, insulin glargine has a daily peak-to-trough ratio of ~2. Over the 6-week duration, the 7-point glucose profiles remained constant over time and were similar to insulin glargine profiles. BIF was well tolerated and had a safety profile similar to insulin glargine-treated subjects. In particular, hypoglycemia rates were also similar to insulin glargine and there was no occurrence of hypoglycemic events with cognitive dysfunction. These data support continued development of BIF as a once-weekly insulin treatment of diabetes mellitus.
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- 2021
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13. Evaluation of Various Static In Vitro–In Vivo Extrapolation Models for Risk Assessment of the CYP3A Inhibition Potential of an Investigational Drug
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Ping Zhao, Richard E. Higgs, Shiew-Mei Huang, Brian J. Kirby, Isabelle Ragueneau-Majlessi, Lei Zhang, David R. Plowchalk, R S Obach, Heidi J. Einolf, E. Seibert, Eva Gil Berglund, Kellie S. Reynolds, Adrian J. Fretland, Stephen D. Hall, Karthik Venkatakrishnan, Jenny Y. Chien, V. Fischer, Ken Grime, T. H. Waterhouse, L T Vieira, Jan Snoeys, Odette A. Fahmi, K. Skordos, Aleksandra Galetin, and R. Riley
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Pharmacology ,Investigational drug ,CYP3A ,Chemistry ,Area under the curve ,Organ specific ,medicine ,Midazolam ,Cutoff ,Pharmacology (medical) ,In vitro in vivo ,Risk assessment ,medicine.drug - Abstract
Nine static models (seven basic and two mechanistic) and their respective cutoff values used for predicting cytochrome P450 3A (CYP3A) inhibition, as recommended by the US Food and Drug Administration and the European Medicines Agency, were evaluated using data from 119 clinical studies with orally administered midazolam as a substrate. Positive predictive error (PPE) and negative predictive error (NPE) rates were used to assess model performance, based on a cutoff of 1.25-fold change in midazolam area under the curve (AUC) by inhibitor. For reversible inhibition, basic models using total or unbound systemic inhibitor concentration [I] had high NPE rates (46-47%), whereas those using intestinal luminal ([I]gut) values had no NPE but a higher PPE. All basic models for time-dependent inhibition had no NPE and reasonable PPE rates (15-18%). Mechanistic static models that incorporate all interaction mechanisms and organ specific [I] values (enterocyte and hepatic inlet) provided a higher predictive precision, a slightly increased NPE, and a reasonable PPE. Various cutoffs for predicting the likelihood of CYP3A inhibition were evaluated for mechanistic models, and a cutoff of 1.25-fold change in midazolam AUC appears appropriate.
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- 2013
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14. Optimization of Drug-Drug Interaction Study Design: Comparison of Minimal Physiologically Based Pharmacokinetic Models on Prediction of CYP3A Inhibition by Ketoconazole
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Jialin Mao, Bing Han, Stephen D. Hall, and Jenny Y. Chien
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Pharmacology ,Physiologically based pharmacokinetic modelling ,Dose-Response Relationship, Drug ,CYP3A ,Midazolam ,Area under the curve ,Pharmaceutical Science ,Biology ,Models, Biological ,Bioavailability ,Dose–response relationship ,Ketoconazole ,Pharmacokinetics ,Research Design ,Area Under Curve ,medicine ,Cytochrome P-450 CYP3A Inhibitors ,Humans ,Drug Interactions ,Enzyme Inhibitors ,medicine.drug - Abstract
Ketoconazole is a potent CYP3A inhibitor used to assess the contribution of CYP3A to drug clearance and quantify the increase in drug exposure due to a strong inhibitor. Physiologically based pharmacokinetic (PBPK) models have been used to evaluate treatment regimens resulting in maximal CYP3A inhibition by ketoconazole but have reached different conclusions. We compare two PBPK models of the ketoconazole-midazolam interaction, model 1 (Chien et al., 2006) and model 2 implemented in Simcyp (version 11), to predict 16 published treatment regimens. With use of model 2, 41% of the study point estimates of area under the curve (AUC) ratio and 71% of the 90% confidence intervals were predicted within 1.5-fold of the observed, but these increased to 82 and 100%, respectively, with model 1. For midazolam, model 2 predicted a maximal midazolam AUC ratio of 8 and a hepatic fraction metabolized by CYP3A (f(m)) of 0.97, whereas model 1 predicted 17 and 0.90, respectively, which are more consistent with observed data. On the basis of model 1, ketoconazole (400 mg QD) for at least 3 days and substrate administration within 2 hours is required for maximal CYP3A inhibition. Ketoconazole treatment regimens that use 200 mg BID underestimate the systemic fraction metabolized by CYP3A (0.86 versus 0.90) for midazolam. The systematic underprediction also applies to CYP3A substrates with high bioavailability and long half-lives. The superior predictive performance of model 1 reflects the need for accumulation of ketoconazole at enzyme site and protracted inhibition. Model 2 is not recommended for inferring optimal study design and estimation of fraction metabolized by CYP3A.
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- 2013
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15. A Comprehensive Review of Novel Drug-Disease Models in Diabetes Drug Development
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Catharina Link, Puneet Gaitonde, Stephan Schmidt, Jenny Y. Chien, Mirjam N. Trame, and Parag Garhyan
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Drug ,Blood Glucose ,media_common.quotation_subject ,030209 endocrinology & metabolism ,Disease ,Bioinformatics ,030226 pharmacology & pharmacy ,Incretins ,Models, Biological ,Receptors, G-Protein-Coupled ,03 medical and health sciences ,0302 clinical medicine ,Pharmacokinetics ,Glucagon-Like Peptide 1 ,Diabetes mellitus ,Glucokinase ,Receptors, Glucagon ,Medicine ,Glucose homeostasis ,Humans ,Hypoglycemic Agents ,Insulin ,Pharmacology (medical) ,Sodium-Glucose Transporter 2 Inhibitors ,media_common ,Pharmacology ,Glycated Hemoglobin ,Dipeptidyl-Peptidase IV Inhibitors ,business.industry ,Clinical study design ,medicine.disease ,Drug development ,Diabetes Mellitus, Type 2 ,Hyperglycemia ,Biomarker (medicine) ,business ,Biomarkers - Abstract
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease, which affects millions of people worldwide. The disease is characterized by chronically elevated blood glucose concentrations (hyperglycaemia), which result in comorbidities and multi-organ dysfunction. This is due to a gradual loss of glycaemic control as a result of increasing insulin resistance, as well as decreasing β-cell function. The objective of T2DM drug interventions is, therefore, to reduce fasting and postprandial blood glucose concentrations to normal, healthy levels without hypoglycaemia. Several classes of novel antihyperglycaemic drugs with various mechanisms of action have been developed over the past decades or are currently under clinical development. The development of these drugs is routinely supported by the application of pharmacokinetic/pharmacodynamic modelling and simulation approaches. They integrate information on the drug’s pharmacokinetics, clinically relevant biomarker information and disease progression into a single, unifying approach, which can be used to inform clinical study design, dose selection and drug labelling. The objective of this review is to provide a comprehensive overview of the quantitative approaches that have been reported since the 2008 review by Landersdorfer and Jusko in an increasing order of complexity, starting with glucose homeostasis models. Each of the presented approaches is discussed with respect to its strengths and limitations, and respective knowledge gaps are highlighted as potential opportunities for future drug–disease model development in the area of T2DM.
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- 2016
16. PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: Prediction of plasma concentration–time profiles in human by using the physiologically‐based pharmacokinetic modeling approach
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Volker Fischer, Christopher R. Gibson, Handan He, Kimberly K. Adkison, Patrick Poulin, Hannah M. Jones, Sandeep Dutta, Rhys D.O. Jones, M. Sherry Ku, Vikash K. Sinha, Ragini Vuppugalla, Punit Marathe, Thierry Lavé, Jenny Y. Chien, Malcolm Rowland, Thorir Björnsson, Barbara J. Ring, and James W.T. Yates
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Physiologically based pharmacokinetic modelling ,Databases, Pharmaceutical ,Metabolic Clearance Rate ,Pharmacokinetic modeling ,Drug Evaluation, Preclinical ,Cmax ,Administration, Oral ,Pharmaceutical Science ,Computational biology ,Pharmacology ,Models, Biological ,Access to Information ,Species Specificity ,Pharmacokinetics ,Drug Discovery ,Animals ,Humans ,Distribution (pharmacology) ,Computer Simulation ,Cooperative Behavior ,Program Development ,Pharmaceutical sciences ,Models, Statistical ,Chemistry ,Reproducibility of Results ,Pharmaceutical Preparations ,Drug development ,Gastrointestinal Absorption ,Plasma concentration ,Administration, Intravenous ,Interdisciplinary Communication ,Program Evaluation - Abstract
The objective of this study is to assess the effectiveness of physiologically based pharmacokinetic (PBPK) models for simulating human plasma concentration-time profiles for the unique drug dataset of blinded data that has been assembled as part of a Pharmaceutical Research and Manufacturers of America initiative. Combinations of absorption, distribution, and clearance models were tested with a PBPK approach that has been developed from published equations. An assessment of the quality of the model predictions was made on the basis of the shape of the plasma time courses and related parameters. Up to 69% of the simulations of plasma time courses made in human demonstrated a medium to high degree of accuracy for intravenous pharmacokinetics, whereas this number decreased to 23% after oral administration based on the selected criteria. The simulations resulted in a general underestimation of drug exposure (Cmax and AUC0- t ). The explanations for this underestimation are diverse. Therefore, in general it may be due to underprediction of absorption parameters and/or overprediction of distribution or oral first-pass. The implications of compound properties are demonstrated. The PBPK approach based on in vitro-input data was as accurate as the approach based on in vivo data. Overall, the scientific benefit of this modeling study was to obtain more extensive characterization of predictions of human PK from PBPK methods.
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- 2011
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17. LY2189265, a long-acting glucagon-like peptide-1 analogue, showed a dose-dependent effect on insulin secretion in healthy subjects
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F. Tibaldi, Jenny Y. Chien, B. Ellis, H. D. H. Showalter, K. Schneck, and P. Barrington
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Adult ,Male ,medicine.medical_specialty ,Adolescent ,Injections, Subcutaneous ,Recombinant Fusion Proteins ,Endocrinology, Diabetes and Metabolism ,medicine.medical_treatment ,Glucagon-Like Peptides ,Cmax ,Pharmacology ,Young Adult ,Endocrinology ,Double-Blind Method ,Pharmacokinetics ,Glucagon-Like Peptide 1 ,Internal medicine ,Diabetes mellitus ,Internal Medicine ,medicine ,Humans ,Hypoglycemic Agents ,Insulin ,Glucose tolerance test ,Cross-Over Studies ,Dose-Response Relationship, Drug ,medicine.diagnostic_test ,business.industry ,Glucose Tolerance Test ,Middle Aged ,medicine.disease ,Crossover study ,Immunoglobulin Fc Fragments ,Treatment Outcome ,Diabetes Mellitus, Type 2 ,Tolerability ,Area Under Curve ,Pharmacodynamics ,Female ,business ,Half-Life - Abstract
Aim: To assess the safety, tolerability, pharmacokinetics, pharmacodynamics and potential immunogenicity of single, escalating subcutaneous injections of a once-weekly glucagon-like peptide-1 analogue in healthy subjects. Methods: This phase 1, three-period, crossover, double-blind, placebo-controlled study investigated single, escalating subcutaneous doses of LY2189265 (LY) ranging from 0.1 to 12 mg; approximately six subjects were randomized to each dose. Parameters of safety, including adverse events, were assessed. The pharmacokinetic profile was assessed over 14 days. Pharmacodynamic parameters (glucose and insulin concentrations) were measured following a step-glucose infusion (day 3) and as part of an oral glucose tolerance test (OGTT) (day 5). Results: LY was generally well tolerated with some increase in gastrointestinal symptoms with escalating doses. There were small dose-dependent increases in pulse rate with doses ≥1.0 mg and diastolic blood pressure with doses ≥3.0 mg. The half-life of LY was approximately 90 h, with Cmax occurring between 24 and 48 h in most subjects. Evidence of increase in glucose-dependent insulin secretion and suppression of serum glucose excursions were observed during an OGTT at all doses compared to placebo; no episodes of hypoglycaemia occurred. No subjects developed antibodies to LY2189265. Conclusions: LY showed an acceptable safety profile and exhibited the expected glucagon-like peptide-1 pharmacological effects on glucose suppression and insulin secretion with a half-life that supports once-weekly dosing.
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- 2011
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18. A 5-week study of the pharmacokinetics and pharmacodynamics of LY2189265, a novel, long-acting glucagon-like peptide-1 analogue, in patients with type 2 diabetes
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Thomas Hardy, S. Cui, F. Tibaldi, B. Ellis, H. D. H. Showalter, P. Barrington, K. Schneck, and Jenny Y. Chien
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Adult ,Blood Glucose ,Male ,medicine.medical_specialty ,Recombinant Fusion Proteins ,Endocrinology, Diabetes and Metabolism ,Glucagon-Like Peptides ,Type 2 diabetes ,Pharmacology ,Drug Administration Schedule ,Cohort Studies ,Endocrinology ,Double-Blind Method ,Pharmacokinetics ,Glucagon-Like Peptide 1 ,Internal medicine ,Internal Medicine ,medicine ,Humans ,Hypoglycemic Agents ,Aged ,Acarbose ,Dose-Response Relationship, Drug ,business.industry ,Area under the curve ,Fasting ,Middle Aged ,Postprandial Period ,Repaglinide ,medicine.disease ,Metformin ,Immunoglobulin Fc Fragments ,Treatment Outcome ,Postprandial ,Diabetes Mellitus, Type 2 ,Area Under Curve ,Pharmacodynamics ,Female ,Thiazolidinediones ,business ,medicine.drug - Abstract
Aim: To investigate the safety, tolerability, pharmacokinetics and pharmacodynamics of LY2189265 (LY), a novel, long-acting glucagen-like peptide-1 analogue, administered once weekly to subjects with type 2 diabetes. Methods: This was a placebo-controlled, parallel-group, subject- and investigator-blind study of LY in subjects (N = 43) with type 2 diabetes mellitus controlled with diet and exercise alone or with a single oral antidiabetic medication. Subjects taking metformin or thiazolidinediones continued on their therapy. Subjects receiving sulfonylurea, acarbose, repaglinide or nateglinide were switched to metformin prior to enrollment. Subjects received five once-weekly doses of 0.05, 0.3, 1, 3, 5 or 8 mg. Effects on glucose, insulin and C-peptide concentrations were determined during fasting and following standard test meals. The pharmacokinetics of LY and its effects on HBA1c, glucagon, body weight, gastric emptying and safety parameters were assessed. Results: Once-weekly administration of LY significantly reduced (p < 0.01) fasting plasma glucose, 2-h post-test meal postprandial glucose and area under the curve (AUC) of glucose after test meals at doses ≥1 mg. These effects were seen after the first dose and were sustained through the weekly dosing cycle. Most doses produced statistically significant increases in insulin and C-peptide AUC when normalized for glucose AUC. Statistically significant reductions in HBA1c were observed for all dose groups except 0.3 mg. The most commonly reported adverse effects (AEs) were nausea (35 events), headache (20 events), vomiting (18 events) and diarrhoea (8 events). Conclusions: LY showed improvement in fasting and postprandial glycaemic parameters when administered once weekly in subjects with type 2 diabetes. The pharmacokinetics and safety profiles also support further investigation of this novel agent.
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- 2011
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19. Desipramine, substrate for CYP2D6 activity: population pharmacokinetic model and design elements of drug-drug interaction trials
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Kimberley Jackson, Steven A. Wrighton, Jenny Y. Chien, Ivelina Gueorguieva, and Vikram Sinha
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Pharmacology ,CYP2D6 ,education.field_of_study ,Chemistry ,Population ,Design elements and principles ,Drug interaction ,Pharmacokinetics ,Oral administration ,Desipramine ,medicine ,Pharmacology (medical) ,Reuptake inhibitor ,education ,medicine.drug - Abstract
AIMS To develop a population pharmacokinetic model to describe the pharmacokinetics of desipramine in healthy subjects, after oral administration of a 50 mg dose. Additional objectives were to develop a semi-mechanistic population pharmacokinetic model for desipramine, which allowed simulation of CYP2D6-mediated inhibition, when using desipramine as a probe substrate, and to evaluate certain study design elements, such as duration of desipramine pharmacokinetic sampling, required sample size and optimal pharmacokinetic sampling schedule for intermediate, extensive and ultrarapid metabolizers of CYP2D6 substrates.
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- 2010
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20. Drug delivery trends in clinical trials and translational medicine: Updated analysis of ClinicalTrials.gov database
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Jenny Y. Chien and Rodney J. Y. Ho
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Drug ,Clinical Trials as Topic ,medicine.medical_specialty ,business.industry ,media_common.quotation_subject ,Pharmaceutical Science ,Phases of clinical research ,Pharmacology ,Pre-clinical development ,Clinical trial ,Drug Delivery Systems ,Targeted drug delivery ,Drug development ,Drug delivery ,medicine ,Humans ,Intensive care medicine ,business ,ADME ,media_common - Abstract
While the number of clinical trials has continued to grow by about 20% in the past six months, no corresponding growth in product approval by the food and drug administration is seen or anticipated in the near future. Late-stage clinical failures due to lack of efficacy or toxicity continues to be a challenge. The optimization of absorption, distribution, metabolism and elimination (ADME) has improved drug candidate selection and reduced early clinical failure. The current challenge is how to avoid late stage clinical failures. Expanded knowledge of drug target distribution, pharmacokinetics and validated biomarkers will allow implementation of appropriate drug delivery and clinical trial designs to reduce drug exposure to off-target organs such as the liver and kidney and could reduce potential untoward effects. In essence, integration of drug delivery and targeting to reduce exposure in off-target tissues in the preclinical and clinical program may hold the key to increasing the odds of success in drug development. In this update, we briefly review data on clinical trials pertinent to drug delivery in the current regulatory environment. It also provides our analysis on the emerging trends in second generation antibody therapeutics in drug delivery and targeting.
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- 2009
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21. A new probabilistic rule for drug–dug interaction prediction
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Zhiping Wang, Seongho Kim, Aroonrut Lucksiri, Zhaohui Qin, Lang Li, Jihao Zhou, Menggang Yu, Sara K. Quinney, Jenny Y. Chien, and Stephen D. Hall
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Male ,Midazolam ,Monte Carlo method ,Sample (statistics) ,Bayesian inference ,Article ,Bayes' theorem ,Meta-Analysis as Topic ,Statistics ,Econometrics ,Humans ,Computer Simulation ,Drug Interactions ,Pharmacokinetics ,Probability ,Mathematics ,Pharmacology ,Models, Statistical ,Sampling (statistics) ,Bayes Theorem ,Rule-based system ,Fasting ,Ketoconazole ,Area Under Curve ,Cytochrome P-450 CYP3A Inhibitors ,Female ,Constant (mathematics) ,Monte Carlo Method ,Algorithms ,Probabilistic rule - Abstract
An innovative probabilistic rule is proposed to predict the clinical significance or clinical insignificance of DDI. This rule is coupled with a hierarchical Bayesian model approach to summarize substrate/inhibitor's PK models from multiple published resources. This approach incorporates between-subject and between-study variances into DDI prediction. Hence, it can predict both population-average and subject-specific AUCR. The clinically significant DDI, weak DDI, and clinically insignificant inhibitions are decided by the probabilities of predicted AUCR falling into three intervals, (-infinity, 1.25), (1.25, 2), and (2, infinity). The main advantage of this probabilistic rule to predict clinical significance of DDI over the deterministic rule is that the probabilistic rule considers the sample variability, and the decision is independent of sampling variation; while deterministic rule based decision will vary from sample to sample. The probabilistic rule proposed in this paper is best suited for the situation when in vivo PK studies and models are available for both the inhibitor and substrate. An early decision on clinically significant or clinically insignificant inhibition can avoid additional DDI studies. Ketoconazole and midazolam are used as an interaction pair to illustrate our idea. AUCR predictions incorporating between-subject variability always have greater variances than population-average AUCR predictions. A clinically insignificant AUCR at population-average level is not necessarily true when considering between-subject variability. Additional simulation studies suggest that predicted AUCRs highly depend on the interaction constant K(i) and dose combinations.
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- 2009
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22. Clinical Pharmacokinetics of Dulaglutide in Patients with Type 2 Diabetes: Analyses of Data from Clinical Trials
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Jenny Y. Chien, Michael Heathman, Xuewei Cui, Amparo de la Peña, Corina Loghin, Jeanne S. Geiser, and Jennifer A. Martin
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Adult ,Male ,Coefficient of variation ,Recombinant Fusion Proteins ,Population ,Glucagon-Like Peptides ,030209 endocrinology & metabolism ,Type 2 diabetes ,030204 cardiovascular system & hematology ,Pharmacology ,law.invention ,03 medical and health sciences ,Young Adult ,0302 clinical medicine ,Pharmacokinetics ,law ,Diabetes mellitus ,Medicine ,Humans ,Hypoglycemic Agents ,Pharmacology (medical) ,education ,Aged ,Aged, 80 and over ,education.field_of_study ,Clinical pharmacology ,business.industry ,Drug Administration Routes ,Middle Aged ,medicine.disease ,Immunoglobulin Fc Fragments ,Clinical trial ,Diabetes Mellitus, Type 2 ,Dulaglutide ,Female ,business ,medicine.drug - Abstract
Dulaglutide is a long-acting glucagon-like peptide-1 receptor agonist administered as once-weekly subcutaneous injections for the treatment of type 2 diabetes (T2D). The clinical pharmacokinetics of dulaglutide were characterized in patients with T2D and healthy subjects. The pharmacokinetics of dulaglutide were assessed throughout clinical development, including conventional pharmacokinetic analysis in clinical pharmacology studies and population pharmacokinetic analyses of data from combined phase 2 and phase 3 studies in patients with T2D. The effects of potential covariates on dulaglutide population pharmacokinetics were evaluated using nonlinear mixed-effects models. Dulaglutide gradually reached the maximum concentration in 48 h and had a terminal elimination half-life of 5 days. Steady state was achieved between the second and fourth doses. The accumulation ratio was 1.56 for the 1.5 mg dose. Intra-individual variability estimates for the area under the plasma concentration–time curve and the maximum concentration were both
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- 2015
23. STOCHASTIC PREDICTION OF CYP3A-MEDIATED INHIBITION OF MIDAZOLAM CLEARANCE BY KETOCONAZOLE
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Aroonrut Lucksiri, Steven A. Wrighton, Charles S. Ernest, Jenny Y. Chien, Stephen D. Hall, and J. Christopher Gorski
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CYP3A ,Midazolam ,Pharmaceutical Science ,Pharmacology ,Multiple dosing ,Models, Biological ,Sex Factors ,Pharmacokinetics ,medicine ,Cytochrome P-450 CYP3A ,Humans ,Computer Simulation ,Drug Interactions ,Enzyme Inhibitors ,Inhibitory effect ,Stochastic Processes ,Chemistry ,Body Weight ,Age Factors ,Reproducibility of Results ,Interaction studies ,Enzyme inhibition ,Ketoconazole ,Liver ,Cytochrome P-450 CYP3A Inhibitors ,Software ,Forecasting ,medicine.drug - Abstract
Conventional methods to forecast CYP3A-mediated drug-drug interactions have not employed stochastic approaches that integrate pharmacokinetic (PK) variability and relevant covariates to predict inhibition in terms of probability and uncertainty. Empirical approaches to predict the extent of inhibition may not account for nonlinear or non-steady-state conditions, such as first-pass effects or accumulation of inhibitor concentration with multiple dosing. A physiologically based PK model was developed to predict the inhibition of CYP3A by ketoconazole (KTZ), using midazolam (MDZ) as the substrate. The model integrated PK models of MDZ and KTZ, in vitro inhibition kinetics of KTZ, and the variability and uncertainty associated with these parameters. This model predicted the time- and dose-dependent inhibitory effect of KTZ on MDZ oral clearance. The predictive performance of the model was validated using the results of five published KTZ-MDZ studies. The model improves the accuracy of predicting the inhibitory effect of increasing KTZ dosing on MDZ PK by incorporating a saturable KTZ efflux from the site of enzyme inhibition in the liver. The results of simulations using the model supported the KTZ dose of 400 mg once daily as the optimal regimen to achieve maximum inhibition by KTZ. Sensitivity analyses revealed that the most influential variable on the prediction of inhibition was the fractional clearance of MDZ mediated by CYP3A. The model may be used prospectively to improve the quantitative prediction of CYP3A inhibition and aid the optimization of study designs for CYP3A-mediated drug-drug interaction studies in drug development.
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- 2006
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24. Predictions of the In Vivo Clearance of Drugs from Rate of Loss Using Human Liver Microsomes for Phase I and Phase II Biotransformations
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Steven A. Wrighton, Jenny Y. Chien, Michael A. Mohutsky, and Barbara J. Ring
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Metabolic Clearance Rate ,Glucuronidation ,Pharmaceutical Science ,Plasma protein binding ,In Vitro Techniques ,Mass Spectrometry ,Glucuronides ,Pharmacokinetics ,Predictive Value of Tests ,In vivo ,Humans ,Pharmacology (medical) ,Biotransformation ,Pharmacology ,Chromatography ,biology ,Chemistry ,Organic Chemistry ,Substrate (chemistry) ,Blood Proteins ,biology.organism_classification ,Blood proteins ,Pharmaceutical Preparations ,Microsoma ,Microsomes, Liver ,Microsome ,Molecular Medicine ,Oxidation-Reduction ,Algorithms ,Protein Binding ,Biotechnology - Abstract
The utility of in vitro metabolism to accurately predict the clearance of hepatically metabolized drugs was evaluated. Three major goals were: (1) to optimize substrate concentration for the accurate prediction of clearance by comparing to K m value, (2) to prove that clearance of drugs by both oxidation and glucuronidation may be predicted by this method, and (3) to determine the effects of nonspecific microsomal binding and plasma protein binding. The apparent K m values for five compounds along with scaled intrinsic clearances and predicted hepatic clearances for eight compounds were determined using a substrate loss method. Nonspecific binding to both plasma and microsomal matrices were also examined in the clearance calculations. The K m values were well within the 2-fold variability expected for between laboratory comparisons. Using both phase I and/or phase II glucuronidation incubation conditions, the predictions of in vivo clearance using the substrate loss method were shown to correlate with published human clearance values. Of particular interest, for highly bound drugs (>95% plasma protein bound), the addition of a plasma protein binding term increased the accuracy of the prediction of in vivo clearance. The substrate loss method may be used to accurately predict hepatic clearance of drugs.
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- 2006
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25. CYP2E1 activity before and after weight loss in morbidly obese subjects with nonalcoholic fatty liver disease
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Kenneth E. Thummel, E. Patchen Dellinger, Evan D. Kharasch, Jeannine M. Fisher, Kris V. Kowdley, Jenny Y. Chien, and Maurice G. Emery
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Blood Glucose ,Male ,medicine.medical_specialty ,Gastric Bypass ,Severity of Illness Index ,Gastroenterology ,Liver disease ,Weight loss ,Internal medicine ,Weight Loss ,Nonalcoholic fatty liver disease ,medicine ,Humans ,Hepatology ,medicine.diagnostic_test ,Muscle Relaxants, Central ,business.industry ,Cytochrome P-450 CYP2E1 ,medicine.disease ,Obesity ,Obesity, Morbid ,Enzyme Activation ,Fatty Liver ,Chlorzoxazone ,Endocrinology ,Liver ,Liver biopsy ,Female ,Histopathology ,Median body ,medicine.symptom ,Steatosis ,business - Abstract
Previous studies suggest that hepatic cytochrome P450 2E1 (CYP2E1) activity is increased in individuals with chronic alcoholism, nonalcoholic steatohepatitis (NASH), and morbid obesity, and may contribute to liver disease. We studied 16 morbidly obese subjects with varying degrees of hepatic steatosis and 16 normal-weight controls. Obese subjects were evaluated at baseline, 6 weeks, and 1 year after gastroplasty, a procedure that leads to weight loss. Hepatic CYP2E1 activity was assessed by determination of the clearance of chlorzoxazone (CLZ), an in vivo CYP2E1-selective probe. Liver biopsy tissue was obtained during surgery for histopathology. Both the total and unbound oral CLZ clearance (Cl(u)/F) was elevated approximately threefold in morbidly obese subjects compared with controls (P.001). The Cl(u)/F was significantly higher among subjects with steatosis involving50% of hepatocytes, compared with those with steatosis inor =50% of hepatocytes (P =.02). At postoperative week 6 and year 1, the median body mass index (BMI) of subjects who underwent gastroplasty decreased by 11% and 33%, total oral CLZ clearance declined by 16% (P.01) and 46% (P.05), and Cl(u)/F decreased by 18% (P.05) and 35% (P =.16), respectively. Moreover, those subjects with a year 1 BMI30 kg/m(2) exhibited a median Cl(u)/F that was 63% lower (P =.02) than the respective clearance for all other subjects. In conclusion, hepatic CYP2E1 activity is up-regulated in morbidly obese subjects. A positive association between the degree of steatosis and CYP2E1 activity preoperatively and between the extent of obesity and CYP2E1 activity postoperatively, suggests that CYP2E1 induction is related to or caused by hepatic pathology that results from morbid obesity.
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- 2003
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26. Dose-finding results in an adaptive, seamless, randomized trial of once-weekly dulaglutide combined with metformin in type 2 diabetes patients (AWARD-5)
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Zvonko Milicevic, Jenny Y. Chien, Michael Heathman, T. Forst, James H. Anderson, Mary Jane Geiger, Donald Berry, Brenda Gaydos, Scott M. Berry, and Zachary Skrivanek
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Adult ,Male ,medicine.medical_specialty ,Randomization ,Adolescent ,Diet, Reducing ,Endocrinology, Diabetes and Metabolism ,Injections, Subcutaneous ,Recombinant Fusion Proteins ,Urology ,Glucagon-Like Peptides ,Type 2 diabetes ,Placebo ,Glucagon-Like Peptide-1 Receptor ,law.invention ,Young Adult ,Endocrinology ,Randomized controlled trial ,law ,Diet, Diabetic ,Internal Medicine ,Receptors, Glucagon ,Medicine ,Humans ,Hypoglycemic Agents ,Exercise ,Aged ,Dose-Response Relationship, Drug ,business.industry ,Middle Aged ,Overweight ,medicine.disease ,Combined Modality Therapy ,Metformin ,Surgery ,Immunoglobulin Fc Fragments ,Blood pressure ,Diabetes Mellitus, Type 2 ,Sitagliptin ,Hyperglycemia ,Dulaglutide ,Drug Therapy, Combination ,Female ,Anti-Obesity Agents ,business ,medicine.drug - Abstract
Aims AWARD-5 was an adaptive, seamless, double-blind study comparing dulaglutide, a once-weekly glucagon-like peptide-1 (GLP-1) receptor agonist, with placebo at 26 weeks and sitagliptin up to 104 weeks. The study also included a dose-finding portion whose results are presented here. Methods Type 2 diabetes (T2D) patients on metformin were randomized 3 : 1 : 1 to seven dulaglutide doses, sitagliptin (100 mg), or placebo. A Bayesian algorithm was used for randomization and dose selection. Patients were adaptively randomized to dulaglutide doses using available data on the basis of a clinical utility index (CUI) of glycosylated haemoglobin A1c (HbA1c) versus sitagliptin at 52 weeks and weight, pulse rate (PR) and diastolic blood pressure (DBP) versus placebo at 26 weeks. The algorithm randomly assigned patients until two doses were selected. Results Dulaglutide 1.5 mg was determined to be the optimal dose. Dulaglutide 0.75 mg met criteria for the second dose. Dulaglutide 1.5 mg showed the greatest Bayesian mean change from baseline (95% credible interval) in HbA1c versus sitagliptin at 52 weeks −0.63 (−0.98 to −0.20)%. Dulaglutide 2.0 mg showed the greatest placebo-adjusted mean change in weight [−1.99 (−2.88 to −1.20) kg] and in PR [0.78 (-2.10 to 3.80) bpm]. Dulaglutide 1.5 mg showed the greatest placebo-adjusted mean change in DBP [−0.62 (−3.40 to 2.30) mmHg]. Conclusions The Bayesian algorithm allowed for an efficient exploration of a large number of doses and selected dulaglutide doses of 1.5 and 0.75 mg for further investigation in this trial.
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- 2014
27. Drug–Disease Model-Based Development of Therapeutic Agents for Treatment of Diabetes
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Meindert Danhof, Parag Garhyan, Brian Gregory Topp, Jenny Y. Chien, Stephan Schmidt, and Vikram Sinha
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medicine.medical_specialty ,business.industry ,Drug discovery ,Disease progression ,Disease ,medicine.disease ,Diabetes mellitus ,medicine ,Clinical endpoint ,Glucose homeostasis ,Drug-disease ,Intensive care medicine ,business ,Systems pharmacology - Abstract
Diabetes is a chronic progressive spectrum of diseases with debilitating comorbidities that is one of the top ten leading causes of deaths globally. The development and application of drug–disease models have made significant contributions to all stages of discovery and development for the treatment of diabetes. There are various reliable biomarkers that describe the physiological processes involved in the glucose homeostasis and pathophysiological state of inadequate glucose control. These biomarkers can be classified as fast and slow biomarkers that are highly predictive of the long-term clinical outcomes. Drug–disease models have been developed that describe the dynamics of these biomarkers with varying complexities ranging from systems pharmacology to disease progression and cardiovascular outcome measures. In this chapter, the readers are introduced to the disease of diabetes, well understood predictive biomarkers associated with diabetes, available target-specific therapeutic interventions, drug–disease models and their applications, including a detailed case example in systems pharmacology model. Several key drug–disease models are described with details to facilitate implementation for common therapeutic targets. The availability of predictive biomarkers and quantifiable clinical endpoints has made the development and application of drug–disease modeling highly efficient and valuable in drug discovery and development of important therapies for the treatment of diabetes and associated comorbidities.
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- 2014
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28. Ethanol and production of the hepatotoxic metabolite of acetaminophen in healthy adults
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Jenny Y. Chien, Howard Ro, Kenneth E. Thummel, Sidney D. Nelson, John T. Slattery, Kenneth E. Lown, and Paul B. Watkins
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Adult ,Male ,Time Factors ,NAPQI ,Metabolite ,Pharmacology ,chemistry.chemical_compound ,Pharmacokinetics ,Reference Values ,Benzoquinones ,medicine ,Humans ,Ingestion ,Pharmacology (medical) ,Infusions, Intravenous ,Acetaminophen ,Liver injury ,Cross-Over Studies ,Ethanol ,digestive, oral, and skin physiology ,Cytochrome P-450 CYP2E1 ,Middle Aged ,Models, Theoretical ,CYP2E1 ,medicine.disease ,stomatognathic diseases ,Liver ,chemistry ,Toxicity ,Female ,Imines ,medicine.drug - Abstract
Background Recent case reports suggest that consumption of ethanol may increase the risk of liver injury induced by acetaminophen (INN, paracetamol). However, this possibility is at odds with previous clinical studies that showed that acute ethanol ingestion could protect against hepatotoxicity by inhibiting CYP-mediated acetaminophen oxidation. We tested the hypothesis that ethanol ingestion can increase susceptibility to acetaminophen toxicity if acetaminophen ingestion occurs shortly after ethanol is cleared from the body. Methods Ten healthy volunteers each received a 6-hour intravenous infusion of ethanol (to achieve a blood concentration of 100 mg/dL ethanol) or 5% dextrose in water, administered in random order. Acetaminophen (500 mg) was ingested 8 hours after the end of the infusion. Blood and urine were collected for assessment of formation of N-acetyl-p-benzoquinone imine (NAPQI), the hepatotoxic metabolite of acetaminophen. Results Mean NAPQI formation was enhanced by 22% (range, 2% to 38%; P < .03) when the acetaminophen dose was given after an ethanol infusion, compared with after 5% dextrose in water infusion. This mean increase was similar in magnitude to that predicted by a mathematical model describing the induction of CYP2E1, the main enzyme catalyzing NAPQI formation, by a mechanism of enzyme stabilization. Conclusions Consumption of up to one 750-mL bottle of wine, six 12-ounce cans of beer, or 9 ounces of 80-proof liquor over the course of a single evening modestly increases the fraction of an acetaminophen dose converted to its toxic metabolite, NAPQI, when acetaminophen is ingested soon after ethanol has been cleared from the body. This change in acetaminophen metabolism may present an incremental increase in the risk of acetaminophen hepatotoxicity. Clinical Pharmacology & Therapeutics (2000) 67, 591–599; doi: 10.1067/mcp.2000.106574
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- 2000
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29. Influence of polymorphic N-acetyltransferase phenotype on the inhibition and induction of acetaminophen bioactivation with long-term isoniazid*
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Raimund M. Peter, John T. Slattery, Robert L. Carithers, Jenny Y. Chien, Sidney D. Nelson, Claire Wartell, Charles M. Nolan, and Kenneth E. Thummel
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Adult ,Genotype ,NAPQI ,Arylamine N-Acetyltransferase ,Antitubercular Agents ,Pharmacology ,Drug Administration Schedule ,Pharmacokinetics ,Oral administration ,Isoniazid ,medicine ,Humans ,Pharmacology (medical) ,Antipyretic ,Acetaminophen ,Polymorphism, Genetic ,Chemistry ,digestive, oral, and skin physiology ,Acetylation ,Cytochrome P-450 CYP2E1 ,Analgesics, Non-Narcotic ,Middle Aged ,CYP2E1 ,Drug interaction ,bacterial infections and mycoses ,Phenotype ,medicine.drug - Abstract
Objective To determine in patients receiving isoniazid prophylaxis whether an increase in the CYP2E1-dependent formation clearance of acetaminophen (paracetamol) to N-acetyl-p-benzoquinone imine (NAPQI) occurs during a normal 24-hour isoniazid dose interval and whether the interaction is dependent on acetylation status. Methods Acetaminophen elimination kinetics were determined on four different occasions. Ten subjects were assigned to receive acetaminophen either simultaneously with the 8 am dose of isoniazid or 12 hours after the isoniazid dose. One week later, on the last day of isoniazid therapy, subjects received acetaminophen at the alternate time of day. The control phase acetaminophen administrations were repeated 1 and 2 weeks later, following the initial randomization. Isoniazid acetylation (NAT2) genotype was determined by analysis of genomic DNA obtained from peripheral blood leukocytes. Results The mean NAPQI formation clearance was inhibited 57% when acetaminophen and isoniazid were coadministered but was unchanged compared with time-matched control when acetaminophen was given 12 hours after the isoniazid dose. However, when data from subjects was segregated according to isoniazid (INH) acetylation phenotype, the mean ratio of NAPQI formation clearances (+INH/−INH) with 8 PM acetaminophen was significantly higher for fast acetylators compared with slow acetylators (1.36 versus 0.68; p = 0.006). Conclusions Fast metabolizers of isoniazid appeared to clear the inducer or inhibitor from the active site of CYP2E1 more rapidly, which resulted in an increased formation of NAPQI 12 hours after the isoniazid dose. In contrast, formation of NAPQI for slow isoniazid metabolizers remained inhibited. Clinical Pharmacology & Therapeutics (1997) 61, 24–34; doi
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- 1997
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30. Trends in translational medicine and drug targeting and delivery: new insights on an old concept-targeted drug delivery with antibody-drug conjugates for cancers
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Jenny Y. Chien and Rodney J. Y. Ho
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Drug ,Clinical Trials as Topic ,business.industry ,media_common.quotation_subject ,Pharmaceutical Science ,Cancer ,Antibodies, Monoclonal ,Pharmacology ,medicine.disease ,Article ,Clinical trial ,Translational Research, Biomedical ,Drug Delivery Systems ,Pharmacokinetics ,Targeted drug delivery ,Neoplasms ,Drug delivery ,Medicine ,Animals ,Humans ,Magic bullet ,business ,Drug metabolism ,media_common - Abstract
According to the JPS Drug Delivery Clinical Trials Database (jpharmscidatabase.org), 37,738, 14,104, and 8060 clinical trials are registered to evaluate (1) drug delivery technology, (2) biomolecule platform, and (3) drug metabolism and pharmacokinetic (PK)–pharmacodynamic (PD) interactions. These numbers represent a 19%–29% increase since 2012. Within biomolecules in clinical testing, antibodies constitute the majority and approximately 6% carry drug conjugates. Paul Ehrlich introduced the antibody–drug conjugate or “magic bullet” concept about a century ago. A monoclonal antibody (mAb)–drug conjugate Mylotarg was licensed for treating cancer in 2000 and exhibits significant liver toxicity and immune hypersensitivity. Plasma drug instability and a bacterial-derived drug may be partly to blame. Progress in antibody–drug conjugation chemistry, understanding how biologic systems respond to antibody–drug conjugates, and unwavering efforts of scientists have enabled successful development of highly potent and effective second-generation antibody–drug conjugates. With the approval of Adcetris for lymphoma in 2011 and Kadcyla in 2013, about a twofold to fourfold gain in cancer response rate is attributed to drug conjugates. With a demonstrated higher safety profile, many more antibody–drug conjugates are in development. The clinical success of Adcetris and Kadcyla has raised hope that antibody-guided “drug bullets” may be truly “magical” in leading to a cure for cancer. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 103:71–77, 2014
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- 2013
31. An adaptive, dose-finding, seamless phase 2/3 study of a long-acting glucagon-like peptide-1 analog (dulaglutide): trial design and baseline characteristics
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James H. Anderson, Mary Jane Geiger, Brenda Gaydos, Zachary Skrivanek, Jenny Y. Chien, Donald A. Berry, and Scott M. Berry
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Research design ,Male ,Randomization ,Endocrinology, Diabetes and Metabolism ,Recombinant Fusion Proteins ,Biomedical Engineering ,Glucagon-Like Peptides ,Bioengineering ,Phase (combat) ,law.invention ,Dose finding ,Randomized controlled trial ,Double-Blind Method ,law ,Glucagon-Like Peptide 1 ,Internal Medicine ,Medicine ,Humans ,Hypoglycemic Agents ,Simulation ,Symposium ,Dose-Response Relationship, Drug ,business.industry ,Sitagliptin Phosphate ,Bayes Theorem ,Middle Aged ,Triazoles ,Reliability engineering ,Immunoglobulin Fc Fragments ,Clinical trial ,Diabetes Mellitus, Type 2 ,Research Design ,Baseline characteristics ,Pyrazines ,Dulaglutide ,Female ,business ,medicine.drug - Abstract
Dulaglutide (dula, LY2189265) is a once-weekly glucagon-like peptide-1 analog in development for the treatment of type 2 diabetes mellitus. An adaptive, dose-finding, inferentially seamless phase 2/3 study was designed to support the development of this novel diabetes therapeutic. The study is divided into two stages based on two randomization schemes: a Bayesian adaptive scheme (stage 1) and a fixed scheme (stage 2). Stage 1 of the trial employs an adaptive, dose-finding design to lead to a dula dose-selection decision or early study termination due to futility. If dose selection occurs, the study proceeds to stage 2 to allow continued evaluation of the selected dula doses. At completion, the entire study will serve as a confirmatory phase 3 trial. The final study design is discussed, along with specifics pertaining to the actual execution of this study and selected baseline characteristics of the participants.
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- 2013
32. Application of Adaptive Design Methodology in Development of a Long-Acting Glucagon-Like Peptide-1 Analog (Dulaglutide): Statistical Design and Simulations
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James H. Anderson, Brenda Gaydos, Scott M. Berry, Donald A. Berry, Mary Jane Geiger, Jenny Y. Chien, and Zachary Skrivanek
- Subjects
Research design ,Computer science ,Endocrinology, Diabetes and Metabolism ,Recombinant Fusion Proteins ,Bayesian probability ,Biomedical Engineering ,Glucagon-Like Peptides ,Bioengineering ,Machine learning ,computer.software_genre ,law.invention ,Randomized controlled trial ,Double-Blind Method ,law ,Frequentist inference ,Glucagon-Like Peptide 1 ,Covariate ,Internal Medicine ,medicine ,Humans ,Hypoglycemic Agents ,Analysis of covariance ,Symposium ,Dose-Response Relationship, Drug ,business.industry ,Decision rule ,Immunoglobulin Fc Fragments ,Diabetes Mellitus, Type 2 ,Research Design ,Dulaglutide ,Artificial intelligence ,business ,computer ,Algorithms ,medicine.drug - Abstract
Background: Dulaglutide (dula, LY2189265), a long-acting glucagon-like peptide-1 analog, is being developed to treat type 2 diabetes mellitus. Methods: To foster the development of dula, we designed a two-stage adaptive, dose-finding, inferentially seamless phase 2/3 study. The Bayesian theoretical framework is used to adaptively randomize patients in stage 1 to 7 dula doses and, at the decision point, to either stop for futility or to select up to 2 dula doses for stage 2. After dose selection, patients continue to be randomized to the selected dula doses or comparator arms. Data from patients assigned the selected doses will be pooled across both stages and analyzed with an analysis of covariance model, using baseline hemoglobin A1c and country as covariates. The operating characteristics of the trial were assessed by extensive simulation studies. Results: Simulations demonstrated that the adaptive design would identify the correct doses 88% of the time, compared to as low as 6% for a fixed-dose design (the latter value based on frequentist decision rules analogous to the Bayesian decision rules for adaptive design). Conclusions: This article discusses the decision rules used to select the dula dose(s); the mathematical details of the adaptive algorithm—including a description of the clinical utility index used to mathematically quantify the desirability of a dose based on safety and efficacy measurements; and a description of the simulation process and results that quantify the operating characteristics of the design.
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- 2012
33. Drug Delivery Trends in Clinical Trials and Translational Medicine: Growth in Biologic Molecule Development and Impact on Rheumatoid Arthritis, Crohn’s Disease, and Colitis
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Jenny Y. Chien and Rodney J. Y. Ho
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medicine.medical_specialty ,Pharmaceutical Science ,Arthritis ,Disease ,Pharmacology ,Article ,Arthritis, Rheumatoid ,Translational Research, Biomedical ,Drug Delivery Systems ,Crohn Disease ,Drug Discovery ,medicine ,Humans ,Pharmacokinetics ,Colitis ,Intensive care medicine ,Clinical Trials as Topic ,business.industry ,Tumor Necrosis Factor-alpha ,Translational medicine ,medicine.disease ,Clinical trial ,Biopharmaceutical ,Targeted drug delivery ,Rheumatoid arthritis ,business - Abstract
There are 94,709 clinical trials across 179 countries. Approximately half (47,467) are related to the three categories within the scope of the free online resource "Drug Delivery Trends in Clinical Trials and Translational Medicine," which are (1) drug delivery technology and systems, (2) biological molecule platforms, and (3) pharmacokinetic and pharmacodynamic interactions. In this commentary, trends in biological molecule platforms and their impacts are discussed. The sales of top 15 biologic drugs have reached over $63 billion in 2010. In the past 10 years, major pharmaceutical companies have acquired biological molecule platforms and have become integrated biopharmaceutical companies, highlighting the role of biotechnology in driving new therapeutic product development. The top three products--Remicade, Enbrel, and Humira--indicated for arthritis and colitis and targeted to tumor necrosis factor-alpha (TNF-α), each generated over $6 billion in annual sales. In addition to TNF-α, biologic candidates targeted to other inflammatory molecules are in clinical development, partly driven by commercial interests and medical need. Although clinical experience indicates that all the anti-TNF-α molecular platforms are effective for rheumatoid arthritis, Crohn's disease, and colitis, whether the new agents can provide additional relief or cures remains to be seen.
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- 2012
34. The Application of Drug-Disease Models in the Development of Anti-Hyperglycemic Agents
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Jenny Y. Chien and Vikram Sinha
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medicine.medical_specialty ,business.industry ,Insulin ,medicine.medical_treatment ,Disease ,medicine.disease ,Anti hyperglycemic ,Clinical trial ,Chronic disease ,Drug development ,Diabetes mellitus ,Medicine ,Drug-disease ,business ,Intensive care medicine - Abstract
Diabetes is a chronic disease characterized by hyperglycemia resulting from defects in the regulation of glucose and insulin homeostasis. Hyperglycemia, if not well controlled, will progress to more serious complications. Therefore, all available treatments aim to lower blood glucose by various mechanisms of action. Glucose and glycosylated hemoglobin (HbA1c) are well established and readily measurable biomarkers of the disease. The application of model-based approaches to optimize patient therapy and to gain understanding of the physiology of glucose-insulin regulation is widely accepted in the area of diabetes research and development. In this chapter, we attempt to give a brief overview of the disease and the types of drug-disease models that may be applied in various stages of drug development, including references to key publications of drug-disease models. Through simulations, these models are the essential tools to aid the optimization of clinical trials and to learn about the safety and efficacy of new drugs relative to the standards of care and to face the increasing challenges of drug development for the treatment of diabetes.
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- 2010
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35. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 4: prediction of plasma concentration-time profiles in human from in vivo preclinical data by using the Wajima approach
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Volker Fischer, Barbara J. Ring, Punit Marathe, Hannah M. Jones, M. Sherry Ku, Kimberly K. Adkison, Rhys D.O. Jones, Vikash K. Sinha, Thorir Björnsson, Christopher R. Gibson, Handan He, Thierry Lavé, Patrick Poulin, Sandeep Dutta, Jenny Y. Chien, James W.T. Yates, and Ragini Vuppugalla
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Quantitative structure–activity relationship ,Physiologically based pharmacokinetic modelling ,Databases, Pharmaceutical ,Metabolic Clearance Rate ,Drug Evaluation, Preclinical ,Pharmaceutical Science ,Administration, Oral ,Biological Availability ,Pharmacology ,Models, Biological ,Access to Information ,Intercept method ,Dogs ,Pharmacokinetics ,Species Specificity ,Statistics ,Drug Discovery ,Distribution (pharmacology) ,Animals ,Humans ,Computer Simulation ,Cooperative Behavior ,Program Development ,Volume of distribution ,Models, Statistical ,Chemistry ,Reproducibility of Results ,Confidence interval ,Bioavailability ,Rats ,Pharmaceutical Preparations ,Gastrointestinal Absorption ,Administration, Intravenous ,Interdisciplinary Communication ,Program Evaluation - Abstract
The objective of this study was to evaluate the performance of the Wajima allometry (Css -MRT) approach published in the literature, which is used to predict the human plasma concentration-time profiles from a scaling of preclinical species data. A diverse and blinded dataset of 108 compounds from PhRMA member companies was used in this evaluation. The human intravenous (i.v.) and oral (p.o.) pharmacokinetics (PK) data were available for 18 and 107 drugs, respectively. Three different scenarios were adopted for prediction of human PK profiles. In the first scenario, human clearance (CL) and steady-state volume of distribution (Vss ) were predicted by unbound fraction corrected intercept method (FCIM) and Oie-Tozer (OT) approaches, respectively. Quantitative structure activity relationship (QSAR)-based approaches (TSrat-dog ) based on compound descriptors together with rat and dog data were utilized in the second scenario. Finally, in the third scenario, CL and Vss were predicted using the FCIM and Jansson approaches, respectively. For the prediction of oral pharmacokinetics, the human bioavailability and absorption rate constant were assumed as the average of preclinical species. Various statistical techniques were used for assessing the accuracy of the simulation scenarios. The human CL and Vss were predicted within a threefold error range for about 75% of the i.v. drugs. However, the accuracy in predicting key p.o. PK parameters appeared to be lower with only 58% of simulations falling within threefold of observed parameters. The overall ability of the Css -MRT approach to predict the curve shape of the profile was in general poor and ranged between low to medium level of confidence for most of the predictions based on the selected criteria.
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- 2010
36. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 1: goals, properties of the PhRMA dataset, and comparison with literature datasets
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Punit Marathe, M. Sherry Ku, Vikash K. Sinha, Kimberly K. Adkison, Thorir Björnsson, Thierry Lavé, Handan He, Patrick Poulin, Sandeep Dutta, Volker Fischer, Christopher R. Gibson, Barbara J. Ring, James W.T. Yates, Ragini Vuppugalla, Hannah M. Jones, Rhys D.O. Jones, and Jenny Y. Chien
- Subjects
Physiologically based pharmacokinetic modelling ,Databases, Pharmaceutical ,Drug Evaluation, Preclinical ,Pharmaceutical Science ,Administration, Oral ,Pharmacology ,Machine learning ,computer.software_genre ,Models, Biological ,Risk Assessment ,Access to Information ,Pharmacokinetics ,Species Specificity ,Risk Factors ,Prediction methods ,Drug Discovery ,Medicine ,Animals ,Humans ,Computer Simulation ,Cooperative Behavior ,Program Development ,Models, Statistical ,business.industry ,Reproducibility of Results ,First in human ,Preclinical data ,Pharmaceutical Preparations ,Administration, Intravenous ,Interdisciplinary Communication ,Artificial intelligence ,business ,computer ,Program Evaluation - Abstract
This study is part of the Pharmaceutical Research and Manufacturers of America (PhRMA) initiative on predictive models of efficacy, safety, and compound properties. The overall goal of this part was to assess the predictability of human pharmacokinetics (PK) from preclinical data and to provide comparisons of available prediction methods from the literature, as appropriate, using a representative blinded dataset of drug candidates. The key objectives were to (i) appropriately assemble and blind a diverse dataset of in vitro, preclinical in vivo, and clinical data for multiple drug candidates, (ii) evaluate the dataset with empirical and physiological methodologies from the literature used to predict human PK properties and plasma concentration–time profiles, (iii) compare the predicted properties with the observed clinical data to assess the prediction accuracy using routine statistical techniques and to evaluate prediction method(s) based on the degree of accuracy of each prediction method, and (iv) compile and summarize results for publication. Another objective was to provide a mechanistic understanding as to why one methodology provided better predictions than another, after analyzing the poor predictions. A total of 108 clinical lead compounds were collected from 12 PhRMA member companies. This dataset contains intravenous (n = 19) and oral pharmacokinetic data (n = 107) in humans as well as the corresponding preclinical in vitro, in vivo, and physicochemical data. All data were blinded to protect the anonymity of both the data and the company submitting the data. This manuscript, which is the first of a series of manuscripts, summarizes the PhRMA initiative and the 108 compound dataset. More details on the predictability of each method are reported in companion manuscripts. © 2011 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:4050–4073, 2011
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- 2010
37. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 3: comparative assessement of prediction methods of human clearance
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Christopher R. Gibson, Hannah M. Jones, James W.T. Yates, Jenny Y. Chien, Kimberly K. Adkison, M. Sherry Ku, Vikash K. Sinha, Patrick Poulin, Punit Marathe, Malcolm Rowland, Handan He, Ragini Vuppugalla, Rhys D.O. Jones, Thorir Björnsson, Barbara J. Ring, Sandeep Dutta, Thierry Lavé, and Volker Fischer
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Databases, Pharmaceutical ,Metabolic Clearance Rate ,Drug Evaluation, Preclinical ,Pharmaceutical Science ,Pharmacology ,Models, Biological ,Access to Information ,Animal data ,Dogs ,Pharmacokinetics ,Species Specificity ,In vivo ,Prediction methods ,Drug Discovery ,Animals ,Humans ,Computer Simulation ,Pharmaceutical sciences ,Cooperative Behavior ,Program Development ,Models, Statistical ,Chemistry ,Area under the curve ,Reproducibility of Results ,Preclinical data ,Bioavailability ,Rats ,Pharmaceutical Preparations ,Area Under Curve ,Administration, Intravenous ,Interdisciplinary Communication ,Program Evaluation ,Protein Binding - Abstract
The objective of this study was to evaluate the performance of various allometric and in vitro-in vivo extrapolation (IVIVE) methodologies with and without plasma protein binding corrections for the prediction of human intravenous (i.v.) clearance (CL). The objective was also to evaluate the IVIVE prediction methods with animal data. Methodologies were selected from the literature. Pharmaceutical Research and Manufacturers of America member companies contributed blinded datasets from preclinical and clinical studies for 108 compounds, among which 19 drugs had i.v. clinical pharmacokinetics data and were used in the analysis. In vivo and in vitro preclinical data were used to predict CL by 29 different methods. For many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. In addition, 66 methods of predicting oral (p.o.) area under the curve (AUCp.o. ) were evaluated for 107 compounds using rational combinations of i.v. CL and bioavailability (F), and direct scaling of observed p.o. CL from preclinical species. Various statistical and outlier techniques were employed to assess the predictability of each method. Across methods, the maximum success rate in predicting human CL for the 19 drugs was 100%, 94%, and 78% of the compounds with predictions falling within 10-fold, threefold, and twofold error, respectively, of the observed CL. In general, in vivo methods performed slightly better than IVIVE methods (at least in terms of measures of correlation and global concordance), with the fu intercept method and two-species-based allometry (rat-dog) being the best performing methods. IVIVE methods using microsomes (incorporating both plasma and microsomal binding) and hepatocytes (not incorporating binding) resulted in 75% and 78%, respectively, of the predictions falling within twofold error. IVIVE methods using other combinations of binding assumptions were much less accurate. The results for prediction of AUCp.o. were consistent with i.v. CL. However, the greatest challenge to successful prediction of human p.o. CL is the estimate of F in human. Overall, the results of this initiative confirmed predictive performance of common methodologies used to predict human CL.
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- 2010
38. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 2: comparative assessment of prediction methods of human volume of distribution
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Rhys D.O. Jones, Hannah M. Jones, Christopher R. Gibson, Kimberly K. Adkison, Volker Fischer, M. Sherry Ku, James W.T. Yates, Thierry Lavé, Vikash K. Sinha, Handan He, Thorir Björnsson, Ragini Vuppugalla, Barbara J. Ring, Punit Marathe, Sandeep Dutta, Jenny Y. Chien, Patrick Poulin, and Malcolm Rowland
- Subjects
Computer science ,Databases, Pharmaceutical ,Drug Evaluation, Preclinical ,Pharmaceutical Science ,Context (language use) ,Pharmacology ,Machine learning ,computer.software_genre ,Models, Biological ,Access to Information ,Dogs ,Pharmacokinetics ,Species Specificity ,In vivo ,Prediction methods ,Drug Discovery ,Animals ,Humans ,Computer Simulation ,Predictability ,Cooperative Behavior ,Program Development ,Volume of distribution ,Models, Statistical ,business.industry ,Reproducibility of Results ,Confidence interval ,Rats ,Pharmaceutical Preparations ,Outlier ,Administration, Intravenous ,Interdisciplinary Communication ,Artificial intelligence ,business ,computer ,Program Evaluation ,Protein Binding - Abstract
The objective of this study was to evaluate the performance of various empirical, semimechanistic and mechanistic methodologies with and without protein binding corrections for the prediction of human volume of distribution at steady state (Vss ). PhRMA member companies contributed a set of blinded data from preclinical and clinical studies, and 18 drugs with intravenous clinical pharmacokinetics (PK) data were available for the analysis. In vivo and in vitro preclinical data were used to predict Vss by 24 different methods. Various statistical and outlier techniques were employed to assess the predictability of each method. There was not simply one method that predicts Vss accurately for all compounds. Across methods, the maximum success rate in predicting human Vss was 100%, 94%, and 78% of the compounds with predictions falling within tenfold, threefold, and twofold error, respectively, of the observed Vss . Generally, the methods that made use of in vivo preclinical data were more predictive than those methods that relied solely on in vitro data. However, for many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. It is recommended to initially use the in vitro tissue composition-based equations to predict Vss in preclinical species and humans, putting the assumptions and compound properties into context. As in vivo data become available, these predictions should be reassessed and rationalized to indicate the level of confidence (uncertainty) in the human Vss prediction. The top three methods that perform strongly at integrating in vivo data in this way were the Oie-Tozer, the rat -dog-human proportionality equation, and the lumped-PBPK approach. Overall, the scientific benefit of this study was to obtain greater characterization of predictions of human Vss from several methods available in the literature.
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- 2010
39. Desipramine, substrate for CYP2D6 activity: population pharmacokinetic model and design elements of drug-drug interaction trials
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Ivelina, Gueorguieva, Kimberley, Jackson, Steven A, Wrighton, Vikram P, Sinha, and Jenny Y, Chien
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Adult ,Male ,Genotype ,Desipramine ,Administration, Oral ,Bayes Theorem ,Middle Aged ,Models, Biological ,Phenotype ,Cytochrome P-450 CYP2D6 ,Cytochrome P-450 CYP2D6 Inhibitors ,Humans ,Female ,Pharmacokinetics ,Enzyme Inhibitors - Abstract
To develop a population pharmacokinetic model to describe the pharmacokinetics of desipramine in healthy subjects, after oral administration of a 50mg dose. Additional objectives were to develop a semi-mechanistic population pharmacokinetic model for desipramine, which allowed simulation of CYP2D6-mediated inhibition, when using desipramine as a probe substrate, and to evaluate certain study design elements, such as duration of desipramine pharmacokinetic sampling, required sample size and optimal pharmacokinetic sampling schedule for intermediate, extensive and ultrarapid metabolizers of CYP2D6 substrates.The mean population estimates of the first order absorption rate constant (k(a) ), apparent clearance (CL/F) and apparent volume of distribution at steady state (V(ss) /F) were 0.15h(-1) , 111 lh(-1) and 2950 l, respectively. Further, using the proposed semi-mechanistic hepatic intrinsic clearance model with Bayesian inference, mean population desipramine hepatic intrinsic clearance was estimated to be 262 lh(-1) with between-subject variability of 84%. d-optimal PK sampling times for intermediate metabolizers were calculated to be approximately 0.25, 24, 75 and 200h. Similar sampling times were found for ultrarapid and extensive metabolizers except that the second d-optimal sample was earlier at 14 and 19h, respectively, compared with 24h for intermediate metabolizers. This difference in sampling times between the three genotypes can be attributed to the different intrinsic clearances and elimination rates.A two compartment population pharmacokinetic model best described desipramine disposition. The semi-mechanistic population model developed is suitable to describe the pharmacokinetic behaviour of desipramine for the dose routinely used in drug-drug interaction (DDI) studies. Based on this meta-analysis of seven trials, a sample size of 21 subjects in cross-over design is appropriate for assessing CYP2D6 interaction with novel compounds.
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- 2010
40. [Untitled]
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Philip R. Mayer, Christopher R. Banfield, Jenny Y. Chien, John T. Slattery, and R. K. Brazzell
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Pharmacology ,Volume of distribution ,Aldose reductase ,biology ,Chemistry ,Organic Chemistry ,Pharmaceutical Science ,Aldose reductase inhibitor ,Loading dose ,Pharmacokinetics ,Enzyme inhibitor ,Tissue binding ,biology.protein ,medicine ,Molecular Medicine ,Distribution (pharmacology) ,Pharmacology (medical) ,Biotechnology ,medicine.drug - Abstract
To investigate the hypothesis that the pharmacokinetics of imirestat, an aldose reductase inhibitor, are influenced by saturable binding to tissues, three experiments were done. (1) The nature of the dose dependence was characterized in rats. Two groups of nine adult male Sprague–Dawley rats received iv 14C-imirestat at doses of 2 or 8 mg/kg. Serial blood samples were obtained over 15 days. Volume of distribution at steady-state was significantly different between the high- and the low-dose groups (0.744 ± 0.103 1 and 1.10 ± 0.228 L, respectively). Clearance was independent of dose over this fourfold range (∼15 ml/hr). (2) The effect of either statil or AL3152, both aldose reductase inhibitors and potential competitors for aldose reductase binding, on the pharmacokinetics of a single 0.2-mg/kg iv dose of imirestat was assessed. A 2.4-mg/kg loading dose of statil was administered and a constant-rate infusion (56 µg/hr/kg) was begun 16 hr before imirestat. A 2-mg/kg loading dose of AL3152 and a constant-rate infusion (115 µg/kg/hr) were also administered 16 hr before imirestat. The infusions were maintained throughout the study. AL3152 administration decreased the imirestat steady-state volume of distribution by a mean of 63%. Statil administration decreased it by a mean of 39%. (3) The dosing regimen of the second study was repeated and, at two sampling times, nine tissues and plasma were obtained from four rats per sampling time for determination of imirestat tissue-to-plasma concentration ratio. The tissue/ plasma imirestat concentration ratio in the adrenals 24 hr after imirestat administration was 56.9 ± 20.0 in the imirestat group, 17.7 ± 1.27 in the statil-coadministered group, and 12.3 ± 2.59 in the AL3152-coadministered group. A similar trend of decrease in the ratios was observed in all tissues at both 24 and 168 hr. The results suggest that a saturable tissue binding phenomenon at least partially accounts for the nonlinear pharmacokinetics of imirestat.
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- 1992
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41. Drug delivery trends in clinical trials and translational medicine
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Jenny Y. Chien and Rodney J. Y. Ho
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Clinical Trials as Topic ,Translational medicine ,Pharmaceutical Science ,Pharmacology ,Drug formulations ,Clinical trial ,Drug Delivery Systems ,Risk analysis (engineering) ,Targeted drug delivery ,Pharmaceutical Preparations ,Drug delivery ,Humans ,Business ,Pharmaceutical sciences ,Early phase ,Clinical evaluation - Abstract
The types and number of delivery technologies, platforms and strategies in the early phase of pharmaceutical research and development are enormous. The drug delivery market in the US is estimated by a number of sources to be in the range of $57–82 billion with 6–9% annual growth rate. Drug delivery may be defined broadly as physical dosage form, molecular design or other physical approaches build on the fundamentals of metabolism, pharmacokinetic/pharmacodynamic interactions. Novel drug delivery platforms and strategies continue to be developed as an essential part of a global effort to improve safety and efficacy of new or existing medicinal products and improving efficacy of those that exhibit narrow therapeutic index. However, only a limited number of these platforms or technologies advance to clinical evaluation. Therefore, a systematic review of drug delivery technologies currently in development could provide an important insight on the progress and the emerging trend in translation of drug delivery concepts into pharmaceutical products. Until recently, there has been no centralized repository of information accessible to the researchers on the ongoing clinical trials with details on drug formulations and delivery platforms. As a part of the FDA Modernization Act of
- Published
- 2008
42. Pharmacokinetics/Pharmacodynamics and the stages of drug development: role of modeling and simulation
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Jenny Y. Chien, Vikram Sinha, Michael Heathman, Stuart Friedrich, and Dinesh P. de Alwis
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Clinical Trials as Topic ,Dose-Response Relationship, Drug ,business.industry ,Pharmaceutical Science ,Pharmacology ,Machine learning ,computer.software_genre ,Models, Biological ,Article ,NONMEM ,Target Response ,Modeling and simulation ,Pharmacokinetics ,Drug development ,Pharmacodynamics ,Medicine ,Humans ,Technology, Pharmaceutical ,In patient ,Computer Simulation ,Artificial intelligence ,business ,computer ,PK/PD models - Abstract
Pharmacokinetic (PK) and pharmacodynamic (PD) modeling and simulation (M&S) are well-recognized powerful tools that enable effective implementation of the learn-and-confirm paradigm in drug development. The impact of PK/PD M&S on decision making and drug development risk management is dependent on the question being asked and on the availability and quality of data accessible at a particular stage of drug development. For instance, M&S methodologies can be used to capture uncertainty and use the expected variability in PK/PD data generated in preclinical species for projection of the plausible range of clinical dose; clinical trial simulation can be used to forecast the probability of achieving a target response in patients based on information obtained in early phases of development. Framing the right question and capturing the key assumptions are critical components of the “learn-and-confirm” paradigm in the drug development process and are essential to delivering high-value PK/PD M&S results. Selected works of PK/PD modeling and simulation from preclinical to phase III are presented as case examples in this article.
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- 2005
43. Ho RJ, Chien JY. 2012. Drug Delivery Trends in Clinical Trials and Translational Medicine: Growth in Biologic Molecule Development and Impact on Rheumatoid Arthritis, Crohn's Disease, and Colitis. J Pharm Sci 101:2668–2674
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Rodney J. Y. Ho and Jenny Y. Chien
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Oncology ,medicine.medical_specialty ,Crohn's disease ,business.industry ,Translational medicine ,Pharmaceutical Science ,medicine.disease ,Clinical trial ,Rheumatoid arthritis ,Internal medicine ,Drug delivery ,Immunology ,medicine ,Colitis ,business - Published
- 2012
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44. a novel adaptive dose-finding study to develop LY2189265, a once-weekly GLP-1 analog
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James H. Anderson, Brenda Gaydos, Zachary Skrivanek, S. Berry, Donald A. Berry, AI Thompson, Jenny Y. Chien, and Mary Jane Geiger
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Oncology ,medicine.medical_specialty ,Dose finding ,Endocrinology ,business.industry ,Endocrinology, Diabetes and Metabolism ,Internal medicine ,Internal Medicine ,medicine ,Once weekly ,General Medicine ,business - Published
- 2009
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45. PIII-55Prediction of clarithromycin nonlinear pharmacokinetics and its interaction with midazolam using a physiologically-based pharmacokinetic model
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David R. Jones, J. C. Gorski, Aroonrut Lucksiri, Sara K. Quinney, Jenny Y. Chien, Stephen D. Hall, and X. Zhang
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Pharmacology ,Pharmacokinetics ,Chemistry ,Nonlinear pharmacokinetics ,Clarithromycin ,medicine ,Midazolam ,Pharmacology (medical) ,medicine.drug - Published
- 2006
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46. Projection of doses for QT-prolongation studies based on modeling of the worst-case inhibition of CYP3A
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Steven A. Wrighton, Stephen D. Hall, and Jenny Y. Chien
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Pharmacology ,Drug ,Clinical pharmacology ,business.industry ,media_common.quotation_subject ,QT interval ,law.invention ,Bioavailability ,Pharmacokinetics ,law ,medicine ,Distribution (pharmacology) ,Pharmacology (medical) ,Ketoconazole ,Dosing ,business ,medicine.drug ,media_common - Abstract
Aims To project the probability of “worst-case scenario” for CYP3A-mediated ketoconazole (K) inhibition of CYP3A drugs based on modeling of pharmacokinetic inhibition effect to select doses for high-dose QT prolongation studies for a low extraction ratio (ER) Drug A (range: 0.01–0.03) and high ER Drug B (range: 0.5–1). Both of these drugs are orally absorbed and cleared by CYP3A by unknown fractions (fm). Methods The predicted K portal vein and systemic concentration were used to drive its inhibitory effect on intrinsic clearance and bioavailability of Drugs A and B. Simulations were performed to test worst-case scenarios, including the duration of K dosing and the range of drug's fm and ER. Results Performance of the model was assessed by comparing simulated trials to actual clinical trial results for A and B given with 200 mg K. The model well predicted the 2-fold (A) and 6-fold (B) AUC increases observed in the trials. Assuming worst-case fm and bioavailability, 400 mg K produced less than doubling of AUC compared to results at 200 mg K for both drugs; maximum inhibition was obtained following 3 daily K doses. Doses for QT studies were then selected to achieve the 90th or the 50th quantile of the distribution of AUC ratios, or a 4- and 8-fold exposure-multiple from the target clinical doses, for Drugs A and B, respectively. Conclusions Model-based projection of the probable worst-case CYP3A inhibition can be used for high-dose QT prolongation study design. Clinical Pharmacology & Therapeutics (2005) 77, P31–P31; doi: 10.1016/j.clpt.2004.12.012
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- 2005
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47. A bivariate confidence interval approach to calculate drug interaction and bioequivalence study power
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Jenny Y. Chien and G. J. Weerakkody
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Pharmacology ,Correlation ,Sample size determination ,Statistics ,Cmax ,Econometrics ,Univariate ,Pharmacology (medical) ,Bivariate analysis ,Bioequivalence ,Equivalence (measure theory) ,Confidence interval ,Mathematics - Abstract
Sample size for drug-drug interaction (DDI) and bioequivalence (BE) studies is usually calculated based on AUC and Cmax as independent parameters. The default acceptance regions using the confidence interval (CI) approach are typically predefined dependent on variability of each parameter. Clinical DDI experience shows that changes in AUC and Cmax are highly correlated regardless of effect size. This may impact the power to establish no-effect (equivalence) for both parameters. Purpose. To evaluate the probability (P) of declaring no-effect for select variability, effect and sample size scenarios, assuming a correlation (r) range from 0.3 to 0.9. Methods. P of different scenarios to declare no-effect was calculated using univariate and bivariate CI approaches for simulated datasets using SAS®. Both approaches were applied to actual clinical data for comparison. Results. P appears to decrease with increasing r regardless of sample size. When no-effect was present, the P of meeting the 0.8–1.25 limits for declaring no-effect for both parameters was lower for the bivariate approach. When an effect was present, P for declaring no-effect was higher for the univariate approach. Conclusions. The bivariate CI approach with a consistent 0.75–1.33 acceptance region is recommended for evaluating DDI and BE studies for both high and low variability drugs, especially when equivalence is expected. The relationship between P and r is dependent on relative variance between Cmax and AUC. Clinical Pharmacology & Therapeutics (2004) 75, P91–P91; doi: 10.1016/j.clpt.2003.11.349
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- 2004
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48. The Effects of LY2405319, an FGF21 Analog, in Obese Human Subjects with Type 2 Diabetes
- Author
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Holger K. Schilske, Jenny Y. Chien, Leonard C. Glass, William L. Holland, Mark A. Deeg, Alexei Kharitonenkov, David E. Moller, Gregory Gaich, Bumol Thomas Frank, and Haoda Fu
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Adult ,Blood Glucose ,Male ,medicine.medical_specialty ,FGF21 ,Adolescent ,Apolipoprotein B ,Physiology ,Type 2 diabetes ,Body Mass Index ,Young Adult ,chemistry.chemical_compound ,Double-Blind Method ,Internal medicine ,Diabetes mellitus ,medicine ,Humans ,Insulin ,Obesity ,Molecular Biology ,Triglycerides ,Aged ,Dyslipidemias ,biology ,Adiponectin ,Cholesterol ,Body Weight ,Cholesterol, HDL ,Cholesterol, LDL ,Cell Biology ,Middle Aged ,Placebo Effect ,medicine.disease ,Fibroblast Growth Factors ,Endocrinology ,Diabetes Mellitus, Type 2 ,chemistry ,biology.protein ,Female ,Dyslipidemia - Abstract
SummaryFibroblast growth factor 21 (FGF21) is a recently discovered metabolic regulator. Exogenous FGF21 produces beneficial metabolic effects in animal models; however, the translation of these observations to humans has not been tested. Here, we studied the effects of LY2405319 (LY), a variant of FGF21, in a randomized, placebo-controlled, double-blind proof-of-concept trial in patients with obesity and type 2 diabetes. Patients received placebo or 3, 10, or 20 mg of LY daily for 28 days. LY treatment produced significant improvements in dyslipidemia, including decreases in low-density lipoprotein cholesterol and triglycerides and increases in high-density lipoprotein cholesterol and a shift to a potentially less atherogenic apolipoprotein concentration profile. Favorable effects on body weight, fasting insulin, and adiponectin were also detected. However, only a trend toward glucose lowering was observed. These results indicate that FGF21 is bioactive in humans and suggest that FGF21-based therapies may be effective for the treatment of selected metabolic disorders.
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