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Brexpiprazole is an oral antipsychotic agent indicated for use in patients with schizophrenia, or as adjunctive treatment for major depressive disorder. As cytochrome P450 (CYP) 2D6 contributes significantly to brexpiprazole metabolism, there is a label-recommended 50% reduction in dose among patients with the CYP2D6 poor metabolizer phenotype. This study uses a whole-body physiologically based pharmacokinetic (PBPK) model to compare the pharmacokinetics of brexpiprazole in patients known to be extensive metabolizers (EMs) and poor metabolizers (PMs). A PBPK model was constructed, verified, and validated against brexpiprazole clinical data, and simulations of 500 subjects were performed to establish the median time to effective concentrations in EMs and PMs. The PBPK simulations captured brexpiprazole PK well and demonstrated significant differences in the time to effective concentrations between EMs and PMs according to the label-recommended titration. Additionally, these simulations suggest that CYP2D6 PMs consistently achieve lower minimum concentrations during the dosing interval than CYP2D6 EMs. Simulations using an alternative dosing strategy of twice-daily dosing (as opposed to once daily) in PMs during the first week of brexpiprazole dosing yielded more consistent plasma concentrations between EMs and PMs, without exceeding the area under the plasma concentration-time curve observed in the EMs. Taken together, the results of these PBPK simulations suggest that product labeling for brexpiprazole titration in CYP2D6 PMs likely overcompensates for the decreased clearance seen in this population. We propose an alternative dosing strategy that decreases the time to effective concentrations and recommend a reevaluation of steady-state PK in this population to potentially allow for higher daily doses in CYP2D6 PMs.

The exposure‐response relationship of direct acting oral anti‐coagulants (DOACs) for bleeding risk is steep relative to ischemic stroke reduction. As a result, small changes in exposure may lead to bleeding events. The overall goal of this project was to determine the effect of critical formulation parameters on the pharmacokinetics (PKs) and thus safety and efficacy of generic DOACs. In this first installment of our overall finding, we developed and verified a physiologically‐based PK (PBPK) model for dabigatran etexilate (DABE) and its metabolites. The model was developed following a middle out approach leveraging available in vitro and in vivo data. External validity of the model was confirmed by overlapping predicted and observed PK profiles for DABE as well as free and total dabigatran for a dataset not used during model development. The verified model was applied to interrogate the impact of modulating the microenvironment pH on DABE systemic exposure. The PBPK exploratory analyses highlighted the high sensitivity of DABE exposure to supersaturation ratio and precipitation kinetics.

A variety of marketed drugs belonging to various therapeutic classes are known to cause nephrotoxicity. Nephrotoxicity can manifest itself in several forms depending on the specific site involved as well as the underlying pathophysiological mechanisms. As they often coexist with other pathophysiological conditions, the steps that can be taken to treat them are often limited. Thus, drug-induced nephrotoxicity remains a major clinical challenge. Prior knowledge of risk factors associated with special patient populations and specific classes of drugs, combined with early diagnosis, therapeutic drug monitoring with dose adjustments, as well as timely prospective treatments are essential to prevent and manage them better. Most incident drug-induced renal toxicity is reversible only if diagnosed at an early stage and treated promptly. Hence, diagnosis at an early stage is the need of the hour to counter it. Significant recent advances in the identification of novel early biomarkers of nephrotoxicity are not beyond limitations. In such a scenario, mathematical modeling and simulation (M&S) approaches may help to better understand and predict toxicities in a clinical setting. This review summarizes pathophysiological mechanisms of drug-induced nephrotoxicity, classes of nephrotoxic drugs, management, prevention, and diagnosis in clinics. Finally, it also highlights some of the recent advancements in mathematical M&S approaches that could be used to better understand and predict drug-induced nephrotoxicity.

Phenytoin demonstrates time-dependent and nonlinear pharmacokinetics (PK) within the therapeutic range of 10 to 20 μg/mL. There are discussions on the relevance of bioequivalence (BE) demonstrated in a single-dose BE study in healthy subjects to exposure under chronic use conditions in patients, particularly given that phenytoin has a narrow therapeutic index. The objective of this study was to quantitatively evaluate the appropriateness of single-dose PK BE through simulations for the phenytoin extended-capsule products. A previously published population PK model was updated to account for the interoccasion variability using the dense PK data of the reference listed drug (Dilantin) from 5 single-dose, fully replicated BE studies (n = 124). BE studies with alternative designs were simulated using the developed PK model and subsequently analyzed accordingly: Scenario 1, multiple-dose, 2-period, crossover BE studies with an average BE approach; Scenario 2, single-dose, 4-period, fully replicated BE studies with a reference-scaled average BE approach as recommended in the product-specific guidance. In both scenarios, hypothetical phenytoin capsules with different formulation-related PK parameters, such as relative bioavailability and absorption rates, were included in the simulations. The results showed that the both scenarios provided the same results with respect to BE conclusions.

Thrombosis remains an important complication for children with single ventricle physiology post-Fontan procedure and effective thromboprophylaxis is an important unmet medical need. To obviate conventional dose-finding studies and expedite clinical development, a rivaroxaban dose regimen for this indication was determined utilizing a model-informed drug development approach. A physiologically based pharmacokinetic (PBPK) rivaroxaban model was used to predict a pediatric dosing regimen that would produce drug exposures similar to that of 10 mg once daily in adults. This regimen was used in an open-label, multicenter Phase 3 study, which investigated the use of rivaroxaban for thromboprophylaxis in post-Fontan patients 2 to 8 years of age. The pharmacokinetics (PK) of rivaroxaban was assessed in Part A (n = 12) and in Part B (n = 64) of UNIVERSE. The safety and efficacy in the rivaroxaban group were compared to those in the acetylsalicylic acid group for 12 months. Pharmacodynamic endpoints were assessed in both parts of the study. Rivaroxaban exposures achieved in Part A and B were similar to the adult reference exposures. Prothrombin time also showed similarity to the adult reference. Exposure-response analysis did not identify a quantitative relationship between rivaroxaban exposures and efficacy/safety outcomes within the observed exposure ranges. A body-weight based dose regimen selected by PBPK modeling was shown in the UNIVERSE study to be appropriate for thromboprophylaxis in the post-Fontan pediatric population. Model-based dose selection can support pediatric drug development and bridge adult dose data to pediatrics, thereby obviating the need for dose-finding studies in pediatric programs. This article is protected by copyright. All rights reserved.

Model-informed precision dosing (MIPD) has become synonymous with modern approaches for individualizing drug therapy, in which the characteristics of each patient are considered as opposed to applying a one-size-fits-all alternative. This review provides a brief account of the current knowledge, practices, and opinions on MIPD while defining an achievable vision for MIPD in clinical care based on available evidence. We begin with a historical perspective on variability in dose requirements and then discuss technical aspects of MIPD, including the need for clinical decision support tools, practical validation, and implementation of MIPD in health care. We also discuss novel ways to characterize patient variability beyond the common perceptions of genetic control. Finally, we address current debates on MIPD from the perspectives of the new drug development, health-care economics, and drug regulations. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 61 is January 7, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Key Points Question What is the association of oral anticoagulants and verapamil hydrochloride or diltiazem hydrochloride with adverse bleeding events in patients with no kidney disease? Findings In this comparative effectiveness study using data from 48 442 patients, rates of bleeding were increased for patients receiving dabigatran etexilate with concomitant verapamil or diltiazem compared with those who were receiving concomitant amlodipine or metoprolol therapy. Other direct oral anticoagulants had no evidence of these drug-drug interactions. Meaning These findings suggest that prescribers may need to avoid P-glycoprotein–related drug-drug interactions with dabigatran regardless of kidney function., Importance Direct oral anticoagulants (DOACs) are purported to have fewer drug-drug interactions than warfarin. However, potential interactions with coprescribed medications are still a safety concern. Verapamil hydrochloride and diltiazem hydrochloride are combined P-glycoprotein (P-gp) and CYP3A4 inhibitors and may be associated with increases in the risk of bleeding with DOACs. Objective To evaluate the risk of bleeding with DOACs and verapamil or diltiazem using an active comparator design. Design, Setting, and Participants A comparative effectiveness active comparator cohort study was conducted using US population-based data (2010-2015) analyzed between January 1 and July 15, 2019. Data were obtained on 48 442 patients with nonvalvular atrial fibrillation who had received an index prescription of dabigatran, rivaroxaban, or apixaban between October 19, 2010, through June 30, 2015, with final follow-up on October 1, 2015. Analysis was restricted to individuals with no history of kidney disease who were receiving standard doses of the DOACs. Exposures Patients with initial prescriptions of DOACs who were receiving verapamil or diltiazem were compared with those receiving amlodipine or metoprolol. Main Outcomes and Measures Overall and gastrointestinal major, moderate, and minor bleeding using primary or secondary diagnoses. Hazard ratios and 95% CIs were estimated using inverse probability of treatment weights in Cox proportional hazards regression models. Results Of the 48 442 patients reviewed, analysis was conducted on 1764 patients receiving DOACs with verapamil or diltiazem compared with 3105 receiving amlodipine and 1793 patients receiving DOACs with verapamil or diltiazem compared with 3224 receiving metoprolol. Depending on the comparison, approximately 60% of the cohort were younger than 65 years and male, which differed by treatment group. Rivaroxaban and apixaban were not associated with increased rates of bleeding for patients receiving verapamil or diltiazem compared with those receiving amlodipine or metoprolol. Among patients receiving dabigatran etexilate, the overall bleeding rate was 52% higher (hazard ratio, 1.52; 95% CI, 1.05-2.20) with verapamil or diltiazem vs amlodipine and 43% higher (hazard ratio, 1.43; 95% CI, 1.02-2.00) vs metoprolol. Bleeding rates for dabigatran with verapamil or diltiazem were higher overall for other bleeding types (244.9 vs 158.4 per 1000 person-years; adjusted hazard ratios of overall GI bleeding: 2.16; 95% CI, 1.30-3.60; minor bleeding: 1.56; 95% CI, 1.07-2.27; and minor GI bleeding: 2.16; 95% CI, 1.29-3.63). Sensitivity analyses showed consistent results for dabigatran when used with verapamil and diltiazem, with magnitudes ranging from 50% to 100% increased hazard rates and no significant results for apixaban or rivaroxaban. Conclusions and Relevance Current US prescribing information only recommends prescribing changes with dabigatran and P-gp inhibitors with lower kidney function. This study found increased bleeding risk associated with dabigatran when used concomitantly with the P-gp inhibitors verapamil and diltiazem in individuals with normal kidney function. Clinicians and patients may need to consider these drug-drug interactions when choosing oral anticoagulation., This comparative effectiveness study evaluates drug-drug interactions between direct oral anticoagulants and verapamil or diltiazem in patients with no history of kidney disease.

The growth in the utilization of systems thinking principles has created a paradigm shift in the regulatory sciences and drug product development. Instead of relying extensively on end product testing and one-size-fits-all regulatory criteria, this new paradigm has focused on building quality into the product by design and fostering the development of product-specific, clinically relevant specifications. In this context, this commentary describes the evolution of bioequivalence regulations up to the current day and discusses the potential of applying a Bayesian-like approach, considering all relevant prior knowledge, to guide regulatory bioequivalence decisions in a patient-centric environment.

Osteoporosis is a disorder of the bones in which they are weakened to the extent that they become more prone to fracture. There are various forms of osteoporosis: some of them are induced by drugs, and others occur as a chronic progressive disorder as an individual gets older. As the median age of the population rises across the world, the chronic form of the bone disease is drawing attention as an important worldwide health issue. Developing new treatments for osteoporosis and comparing them with existing treatments are complicated processes due to current acceptance by regulatory authorities of bone mineral density (BMD) and fracture risk as clinical end points, which require clinical trials to be large, prolonged, and expensive to determine clinically significant impacts in BMD and fracture risk. Moreover, changes in BMD and fracture risk are not always correlated, with some clinical trials showing BMD improvement without a reduction in fractures. More recently, bone turnover markers specific to bone formation and resorption have been recognized that reflect bone physiology at a cellular level. These bone turnover markers change faster than BMD and fracture risk, and mathematically linking the biomarkers via a computational model to BMD and/or fracture risk may help in predicting BMD and fracture risk changes over time during the progression of a disease or when under treatment. Here, we discuss important concepts of bone physiology, osteoporosis, treatment options, mathematical modeling of osteoporosis, and the use of these models by the pharmaceutical industry and the Food and Drug Administration.

There has been controversy regarding whether bioavailability of certain oral oncology drugs should be maximized by taking these medications with food, irrespective of label instructions in the dosing and administration section. To provide insight into this controversy, we conducted an in-depth analysis for oral antineoplastic drugs approved by the Food and Drug Administration in 2000-2016 and identified important issues influencing food labeling decisions. Furthermore, a case study involving sonidegib, a drug approved for locally advanced basal cell carcinoma with a significant food effect on exposure, was used to demonstrate the consequences of failure to adhere to food label recommendations using drug-specific population pharmacokinetic and exposure-toxicity models. In 2000-2009, 80% (4 out of 5) of all approved oral antineoplastics with increased bioavailability in the fed state were labeled as "take on empty stomach." In contrast, we found that in 2010-2016 there is a greater diversity in food recommendations for drugs with increased bioavailability in the fed state. Currently, many oral oncology drugs are given with food to maximize their bioavailability; however, as seen from our case study of sonidegib, failure to fully adhere to label recommendations to either take with food or not could lead to adverse consequences in terms of safety and efficacy.

Development of antiobesity drugs is continuously challenged by high dropout rates during clinical trials. The objective was to develop a population pharmacodynamic model that describes the temporal changes in body weight, considering disease progression, lifestyle intervention, and drug effects. Markov modeling (MM) was applied for quantification and characterization of responder and nonresponder as key drivers of dropout rates, to ultimately support the clinical trial simulations and the outcome in terms of trial adherence. Subjects (n = 4591) from 6 Contrave® trials were included in this analysis. An indirect‐response model developed by van Wart et al was used as a starting point. Inclusion of drug effect was dose driven using a population dose‐ and time‐dependent pharmacodynamic (DTPD) model. Additionally, a population‐pharmacokinetic parameter‐ and data (PPPD)‐driven model was developed using the final DTPD model structure and final parameter estimates from a previously developed population pharmacokinetic model based on available Contrave® pharmacokinetic concentrations. Last, MM was developed to predict transition rate probabilities among responder, nonresponder, and dropout states driven by the pharmacodynamic effect resulting from the DTPD or PPPD model. Covariates included in the models and parameters were diabetes mellitus and race. The linked DTPD‐MM and PPPD‐MM was able to predict transition rates among responder, nonresponder, and dropout states well. The analysis concluded that body‐weight change is an important factor influencing dropout rates, and the MM depicted that overall a DTPD model‐driven approach provides a reasonable prediction of clinical trial outcome probabilities similar to a pharmacokinetic‐driven approach.

Till date, no analytical method published to detect Cisplatin has been validated according to the U.S. Food and Drug Administration (FDA) guidance using liquid chromatography mass spectrometry (LC-MS/MS). We report, a validated LC-MS/MS method for quantitative determination of cisplatin in rat plasma and urine according to FDA guidlines. Cisplatin is a platinum containing compound used for the treatment of different types of cancers. Quantitative determination of cisplatin has been carried out using atomic absorption spectroscopy, high pressure liquid chromatography with phosphorescence, ultra-violet detection, or with inductively coupled plasma mass spectrometry. Few LC-MS/MS methods have been reported for the analysis of cisplatin either for direct quantification or indirect by derivatizing with organic compounds but none of the reported methods have validated the method. The developed and validated assay presented here is a highly sensitive LC-MS/MS method developed and validated for the quantitative determination of cisplatin following derivatization with diethyldithiocarbamate (DDTC) in order to detect platinum (Pt) of cisplatin, suitable for pharmacokinetic studies in rats and to further use it to study human toxicology. Chromatographic separation was achieved using a Poroshell 120 EC-C18 column (3×50mm, 2.7μm) with a binary gradient mobile phase. Quantification was performed on a triple quadruple with electrospray ionization and detection was performed using multiple reaction monitoring. The method has a limit of detection of 1ng/mL, and the quantifiable range was 3-3000ng/mL in rat plasma and urine. The method was accurate and precise with an accuracy and precision for intra-day and inter-day of ±20% for lower limit of quantitation and of ±15% for low, mid and high quality control samples. This method was successfully applied to study the pharmacokinetic profile of cisplatin in rat plasma and urine given a range of doses from 0.5 to 3.5mg/kg.

Hormonal contraceptive agents (HCAs) are widely used throughout the world, and women taking HCAs are likely to take other medications. However, little is known about the clinical effect of most drug‐drug interactions (DDIs) associated with HCAs. A team of interdisciplinary outcomes and pharmacometric researchers from academia and industry jointly engage in a research project to (i) quantitatively elucidate DDI impacts on unintended pregnancies and breakthrough bleeding, and (ii) establish a DDI‐prediction framework to inform optimal use of HCAs.

Pharmacogenomics (PGx), studying the relationship between drug response and genetic makeup of an individual, is accelerating advances in precision medicine. The FDA includes PGx information in the labeling of approved drugs to better inform on their safety and effectiveness. We herein present a summary of PGx information found in 261 prescription drug labeling documents by querying the publicly available FDALabel database. A total of 362 drug–biomarker pairs (DBPs) were identified. We profiled DBPs using frequency of the biomarkers and their therapeutic classes. Four categories of applications (indication, safety, dosing and information) were discussed according to information in labeling. This analysis facilitates better understanding, utilization and translation of PGx information in drug labeling among researchers, healthcare professionals and the public.

A population pharmacokinetic/pharmacodynamic (popPK/PD) model for BIIB059 (anti-blood dendritic cell antigen 2 [anti-BDCA2]), a humanized immunoglobulin G1 monoclonal antibody currently under development for the treatment of SLE and CLE, is presented. BIIB059 binds BDCA2, a plasmacytoid dendritic cell (pDC)-specific receptor that inhibits the production of IFN-I and other inflammatory mediators when ligated. Phase 1 PK and PD data of healthy adult volunteers (HV, n = 87) and SLE subjects (n = 22) were utilized for the development of the popPK/PD model. The data included single and multiple dosing of intravenous and subcutaneous BIIB059. BDCA2 internalization (PD marker) was measured for all subjects by monitoring reduction of BDCA2 on pDC cell surface and used for development of the popPD model. A two-compartment popPK model with linear plus non-linear elimination was found to best describe BIIB059 PK. BDCA2 levels were best captured using an indirect response model with stimulation of the elimination of BDCA2. Clearance in SLE subjects was 25% higher compared to HV (6.87 vs 5.52 mL/h). Bodyweight was identified as only other covariate on clearance and central volume. The estimates of EC50 and Emax were 0.35 μg/mL and 8.92, respectively. No difference in EC50 and Emax was observed between SLE and HV. The popPK/PD model described the data accurately, as evaluated by pcVPCs and bootstrap. The presented popPK/PD model for BIIB059 provides valuable insight into the dynamics and dose–response relationship of BIIB059 for the treatment of SLE and CLE and was used to guide dose selection for the Phase 2 clinical study (NCT02847598).

BACKGROUND AND PURPOSE: Cardiovascular safety is one of the most frequent causes of safety‐related attrition both preclinically and clinically. Preclinical cardiovascular safety is routinely assessed using dog telemetry monitoring key cardiovascular functions. The present research was to develop a semi‐mechanistic modelling platform to simultaneously assess changes in contractility (dPdt(max)), heart rate (HR) and mean arterial pressure (MAP) in preclinical studies. EXPERIMENTAL APPROACH: Data from dPdt(max), HR, preload (left ventricular end‐diastolic pressure [LVEDP]) and MAP were available from dog telemetry studies after dosing with atenolol (n = 27), salbutamol (n = 5), L–N(G)–nitroarginine methyl ester (L–NAME; n = 4), milrinone (n = 4), verapamil (n = 12), dofetilide (n = 8), flecainide (n = 4) and AZ001 (n = 14). Literature model for rat CV function was used for the structural population pharmacodynamic model development. LVEDP was evaluated as covariate to account for the effect of preload on dPdt(max). KEY RESULTS: The model was able to describe drug‐induced changes in dPdt(max), HR and MAP for all drugs included in the developed framework adequately, by incorporating appropriate drug effects on dPdt(max), HR and/or total peripheral resistance. Consistent with the Starling's law, incorporation of LVEDP as a covariate on dPdt(max) to correct for the preload effect was found to be statistically significant. CONCLUSIONS AND IMPLICATIONS: The contractility and haemodynamics semi‐mechanistic modelling platform accounts for diurnal variation, drug‐induced changes and inter‐animal variation. It can be used to hypothesize and evaluate pharmacological effects and provide a holistic cardiovascular safety profile for new drugs.

FDA recommendations to manage polymorphic CYP-mediated drug-drug interactions (DDIs) and gene-drug interactions (GDIs) are typically similar. However, DDIs may not always reliably predict GDIs because the victim drug may have multiple metabolic pathways and the perpetrator drug may affect multiple enzymes or transporters. Consequently, it is of great interest to both the pharmaceutical industry and regulatory agencies to determine if DDI studies can be leveraged to inform GDIs or vice versa for dose adjustment and labeling. The objective of this study was to investigate under what circumstances DDIs can be used to predict GDIs for prototypical CYP2C9, CYP2C19, and CYP2D6 substrates. We investigated model substrates for CYP2D6 (metoprolol, dextromethorphan, atomoxetine, and vortioxetine), CYP2C9 (warfarin, flurbiprofen, and celecoxib), and CYP2C19 (omeprazole and clopidogrel). Data on drug exposure for poor metabolizers (GDI) and for DDIs mediated by strong/moderate inhibitors in extensive metabolizers were collected. The impact of DDIs and GDIs on drug exposure was compared using: (1) a descriptive and (2) a physiologically based pharmacokinetic convergence analysis. Results from both approaches indicate that information on DDIs can be used to reliably predict GDIs for CYP2D6 substrates. The situation is more complex for CYP2C9 and CYP2C19 substrates because dose of the inhibitor (CYP2C9) and potency of the inhibitor (CYP2C19) impact the extent to which perpetrator drugs phenotypically convert extensive metabolizers to poor(er) metabolizers.

Lawrence Lesko speaks to Sarah Miller, Commissioning Editor Lawrence Lesko currently serves as Professor of Pharmaceutics and Director of the Center for Pharmacometrics and Systems Pharmacology at the University of Florida College of Pharmacy at Lake Nona in Orlando (FL, USA). This appointment followed his retirement in July 2011 after 16 years as Director of the Office of Clinical Pharmacology in the Center for Drug Evaluation and Research at the US FDA. While at the FDA, Lesko led the advancement of personalized medicine, through the update of existing drug labels with new genetic information and the development of the Voluntary Genomic Data Submission Program. Lesko has published more than 200 peer-reviewed manuscripts, is a frequent invited national and international speaker, and is the recipient of multiple awards for his achievements relating to clinical pharmacology and personalized medicine.

BIIB059 is a novel humanized monoclonal antibody (mAb) that is currently under development for the treatment of Systemic Lupus Erythematosus and Cutaneous Lupus Erythematosus. BIIB059 is targeted against the blood dendritic cell antigen 2 (BDCA2), a receptor exclusively expressed on the surface of plasmacytoid dendritic cells (pDCs). Herein, we utilized pre-clinical pharmacokinetic (PK) and pharmacodynamic (PD) data to develop a non-human primate (NHP) model and to address whether the NHP model can be successfully scaled to predict the human PK/PD. In particular, PK data from 17 cynomolgus monkeys were utilized for PK model development, wherein BIIB059 was administered intravenously (1 and 10 mg/kg single-dosing and 5 mg/kg multiple-dosing) or subcutaneously (0.2 and 7.5 mg/kg single-dosing). Additionally, PD data (BDCA2 receptor density on pDCs) from 6 cynomolgus monkeys were used for the development of the PD model. The developed NHP two-compartment PK model, linked with an indirect response PD model, was subsequently scaled to humans by combining traditional allometric PK scaling with sensitivity-analysis-driven scaling of the PD. The scaled PK/PD model was then used to simulate the human PK/PD for different dose levels. When clinical data from the BIIB059 Phase I study became available, they were used to evaluate the predictability of the scaled PK/PD model and the model simulations were in agreement with the clinical data. Therefore, the presented approach is suggested to be employed in scaling pre-clinical mAb models to support the selection of safe first-in-human doses and, more broadly, the prediction of PK/PD in the clinic.

Contains fulltext : 200252.pdf (Publisher’s version ) (Open Access) The development of model-informed precision dosing (MIPD) tools, especially in the form of native or web-based applications to be used at the bedside, has garnered marked attention in recent years. Their potential clinical benefit can be large, but it should be ensured that such tools make optimal use of available clinical data and have adequate predictive ability. Unique scientific challenges specific to MIPD remain, which may require adaptation of commonly used diagnostics in pharmacometrics.

Osteonecrosis of the jaw (ONJ) is a serious adverse drug event that was initially reported with intravenous bisphosphonates (BPs) and more recently with other classes of drugs such as receptor activator of NF-κB ligand (RANKL) inhibitor, antiangiogenic agents, and mammalian target of rapamycin (m-TOR) inhibitors. The purpose of this study is to analyze the ONJ cases and the associated drugs in the US Food and Drug Administration's adverse event reporting system (FAERS). The FAERS database was queried for the adverse drug events reported from the first quarter of 2010 to the first quarter of 2014. The reporting odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for each queried drug. A total of 17,119 unique ONJ cases were identified. In the overall analysis, the drugs with the highest reporting ORs were BPs: pamidronate (OR = 498.9), zoledronate (OR = 171.7), and alendronate (OR = 63.6), whereas denosumab had lower ORs than all the BPs except for etidronate. The antiangiogenic and m-TOR inhibitors had the lowest ORs. In cancer patients who were treated for prevention of skeletal-related events (SREs), the reporting ORs for zoledronate and denosumab were 125.2 and 4.9, respectively. In patients with osteoporosis, the ORs were 1.1 (1.0-1.18) for zoledronate and 0.63 (0.56-0.70) for denosumab, respectively. Our analysis of the FAERS database showed that the intravenous BPs were associated with the highest risk for ONJ, RANKL inhibitor was associated with risk comparable to BPs used for osteoporosis such as etidronate, and the antiangiogenic agents and m-TOR inhibitors were associated with the lowest risk for ONJ. The high risk for ONJ with zoledronate and denosumab was mainly observed in those who were treated for prevention of SREs, whereas there was limited evidence for such risk in those who were treated for osteoporosis.

A systems pharmacology model typically integrates pharmacokinetic, biochemical network, and systems biology concepts into a unifying approach. It typically consists of a large number of parameters and reaction species that are interlinked based upon the underlying (patho)physiology and the mechanism of drug action. The more complex these models are, the greater the challenge of reliably identifying and estimating respective model parameters. Global sensitivity analysis provides an innovative tool that can meet this challenge. CPT Pharmacometrics Syst. Pharmacol. (2015) 4, 69–79; doi:10.1002/psp4.6; published online 25 February 2015

This commentary focuses on the status of oral anticoagulants, namely, warfarin and the novel oral anticoagulants (NOACs) such as dabigatran, rivaroxaban, apixaban, and edoxaban.

Reverse translation (RT) refers to perceptions and observations of clinical outcomes, both beneficial and harmful, that can lead to a hypothesis intended to identify a new use of a drug that is different from the original use. I provide a panoramic view of successes in RT from the historic discovery of penicillin to the contemporary development of CAR (chimeric antigen receptor) T therapies. I delineate the core principles of RT that shift discovery from serendipity to a systematic strategy based on target identification and causal biology, pharmacodynamic biomarkers that recapitulate disease pathophysiology, confirmation of target engagement by clinical proof of concept studies, and optimal selection of dose and interval. The article proposes four different categories of RT and successful examples are provided for each category. It concludes with a summary of open questions related to the business case of RT that includes a comparison of the pros, cons, and barriers to future RT development programs.

Introduction: Patiromer is a potassium-binding polymer that is not systemically absorbed; however, it may bind coadministered oral drugs in the gastrointestinal tract, potentially reducing their absorption. Methods: Twelve randomized, open-label, 3-period, 3-sequence crossover studies were conducted in healthy volunteers to evaluate the effect of patiromer (perpetrator drug) on absorption and single-dose pharmacokinetics (PK) of drugs (victims) that might be commonly used with patiromer. Subjects received victim drug alone, victim drug administered together with patiromer 25.2 g (highest approved dose), and victim drug administered 3 hours before patiromer 25.2 g. The primary PK endpoints were area under the curve (AUC), extrapolated to infinity (AUC0-∞), and maximum concentration ( Cmax). Results were reported as 90% confidence intervals (CIs) about the geometric mean AUC0-∞ and Cmax ratios with prespecified equivalence limits of 80% to 125%. Results: Overall, 370 subjects were enrolled, with 365 receiving ≥1 dose of patiromer; 351 subjects completed the studies and all required treatments. When coadministered with patiromer, the 90% CIs for AUC0-∞ remained within 80% to 125% for 9 drugs (amlodipine, cinacalcet, clopidogrel, furosemide, lithium, metoprolol, trimethoprim, verapamil, and warfarin). The AUC0-∞ point estimate ratios for levothyroxine and metformin with patiromer coadministration were ≥80%, with the lower bounds of the 90% CIs at 76.8% and 72.8%, respectively. For ciprofloxacin, the point estimate for AUC0-∞ was 71.5% (90% CI: 65.3-78.4). For 8 of 12 drugs, point estimates for Cmax were ≥80% with patiromer coadministration; for ciprofloxacin, clopidogrel, metformin, and metoprolol, the point estimates were 0-∞ and Cmax for each drug were within the prespecified 80% to 125% limits. Conclusion: For 9 of the 12 drugs coadministered with patiromer, there were no clinically significant drug–drug interactions. For 3 drugs (ciprofloxacin, levothyroxine, and metformin), a 3-hour separation between patiromer and their administration resulted in no clinically significant drug–drug interactions.

While recent discoveries have paved the way for the use of genotype-guided prescribing in some clinical environments, significant debate persists among clinicians and researchers about the optimal approach to pharmacogenetic testing in clinical practice. One crucial factor in this debate surrounds the timing and methodology of genotyping, specifically whether genotyping should be performed reactively for targeted genes when a single drug is prescribed, or preemptively using a panel-based approach prior to drug prescribing. While early clinical models that employed a preemptive approach were largely developed in academic health centers through multidisciplinary efforts, increasing examples of pharmacogenetic testing are emerging in community-based and primary care practice environments. However, educational and practice-based resources for these clinicians remain largely nonexistent. As such, there is a need for the health care system to shift its focus from debating about preemptive genotyping to developing and disseminating needed resources to equip frontline clinicians for clinical implementation of pharmacogenetics. Providing tools and guidance to support these emerging models of care will be essential to support the thoughtful, evidence-based use of pharmacogenetic information in diverse clinical practice environments. Specifically, the creation of efficient and accurate point-of-care resources, practice-based tools, and clinical models is needed, along with identification and dissemination of sustainable avenues for pharmacogenetic test reimbursement.

The University of Florida Center for Pharmacometrics and Systems Pharmacology and the Food and Drug Administration Office of Generic Drugs have collaborated on a research project to develop a mechanism- and risk-based strategy that systematically investigates postmarketing reports of therapeutic inequivalence following the switch between brand and generic drug products. In this study we developed and qualified a physiologically based pharmacokinetic model to systematically investigate the influence of drug- and formulation-related properties on the oral absorption and bioequivalence of modified-release products using metoprolol as an example. Our findings show that the properties of the release-controlling polymer are the critical attributes for in vitro dissolution, in vivo absorption, and systemic exposure (ie, pharmacokinetics) and, thus, the bioequivalence of metoprolol extended-release products rather than the properties of the drug itself. Differences in dissolution rate can result in significant differences in maximum plasma concentration but not in area under the concentration-time curve.

Item does not contain fulltext Patient groups prone to polypharmacy and special subpopulations are susceptible to suboptimal treatment. Refined dosing in special populations is imperative to improve therapeutic response and/or lowering the risk of toxicity. Model-informed precision dosing (MIPD) may improve treatment outcomes by achieving the optimal dose for an individual patient. There is, however, relatively little published evidence of large-scale utility and impact of MIPD, where it is often implemented as local collaborative efforts between academia and healthcare. This article highlights some successful applications of bringing MIPD to clinical care and proposes strategies for wider integration in healthcare. Considerations are brought up herein that will need addressing to see MIPD become "widespread clinical practice," among those, wider interdisciplinary collaborations and the necessity for further evidence-based efficacy and cost-benefit analysis of MIPD in healthcare. The implications of MIPD on regulatory policies and pharmaceutical development are also discussed as part of the roadmap.