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The theme of this month's issue of Clinical Pharmacology & Therapeutics is pharmacometrics. When looking back at the early days of pharmacometrics, current contributions to the drug development process look impressive. The questions are whether the original promise is being kept and how the impact can become even greater. Clinical Pharmacology & Therapeutics (2014); 95 6, 567–571. doi:10.1038/clpt.2014.72

A mathematical pharmacokinetic/anti-drug-antibody (PK/ADA) model was constructed for quantitatively assessing immunogenicity for therapeutic proteins. The model is inspired by traditional pharmacokinetic/pharmacodynamic (PK/PD) models, and is based on the observed impact of ADA on protein drug clearance. The hypothesis for this work is that altered drug PK contains information about the extent and timing of ADA generation. By fitting drug PK profiles while accounting for ADA-mediated drug clearance, the model provides an approach to characterize ADA generation during the study, including the maximum ADA response, sensitivity of ADA response to drug dose level, affinity maturation rate, time lag to observe an ADA response, and the elimination rate for ADA–drug complex. The model also provides a mean to estimate putative concentration–time profiles for ADA, ADA–drug complex, and ADA binding affinity-time profile. When simulating ADA responses to various drug dose levels, bell-shaped dose–response curves were generated. The model contains simultaneous quantitative modeling and provides estimation of the characteristics of therapeutic protein drug PK and ADA responses in vivo. With further experimental validation, the model may be applied to the simulation of ADA response to therapeutic protein drugs in silico, or be applied in subsequent PK/PD models.

Replacement of hydrogen with fluorine within three pairs of structurally similar small molecule inhibitors of heat shock protein 90 (HSP90) resulted in differences in inhibition constants (K(i)) in vitro as well as marked differences in rat intravenous pharmacokinetic profiles. The difference in pharmacokinetic profiles between lower and higher affinity inhibitors (LAIs and HAIs, respectively) was characterized by remarkably different estimates for steady-state volumes of distribution (V(ss): 1.8-2.0 versus 10-13 l/kg) with comparable clearance estimates (3.2-3.5 l/h per kilogram). When the observed V(ss) estimates were compared with the values predicted with the tissue-composition-based model, the observed V(ss) estimates for HAIs were 4- to 8-fold larger than the predicted values, whereas the V(ss) values for LAIs were comparable. Accordingly, a negative relationship between in vitro HSP90 K(i) versus in vivo V(ss) estimates was observed among these inhibitors. We therefore hypothesized that pharmacokinetic profiles of these inhibitors could be characterized by a target-mediated drug disposition (TMDD) model. In vivo equilibrium dissociation constant (K(D)) estimates for HAIs due to target binding by TMDD model with rapid binding approximation were 1-6 nM (equivalent to 0.3-2 nM free drug), which appeared comparable to the in vitro K(i) estimates (2-3 nM). In vivo KD values of LAIs were not accurately determined by the TMDD model, likely due to nonspecific binding-dependent tissue distribution obscuring TMDD profiles. Overall, these results suggest that the observed large Vss estimates for potent HSP90 inhibitors are likely due to pharmacological target binding.

Objective To compare magnesium sulphate concentrations achieved by intramuscular and intravenous regimens used for the prevention of eclampsia. Setting Low-resource obstetric hospitals in Nagpur and Vellore, India. Population Pregnant women at risk for eclampsia due to hypertensive disease. Methods A pharmacokinetic study was performed as part of a randomised trial that enrolled 300 women comparing intramuscular and intravenous maintenance regimens of magnesium dosing. Data from 258 enrolled women were analysed in the pharmacokinetic study. A single sample was drawn per woman with the expectation of using samples in a pooled data analysis. Main outcome measures Pharmacokinetic parameters of magnesium distribution and clearance. Results Magnesium clearance was estimated to be 48.1 dl/hour, volume of distribution to be 156 dl and intramuscular bioavailability to be 86.2%. The intramuscular regimen produced higher initial serum concentrations, consistent with a substantially larger loading dose. At steady state, magnesium concentrations in the intramuscular and intravenous groups were comparable. With either regimen, a substantial number of women would be expected to have serum concentrations lower than those generally held to be therapeutic. Conclusions Clinical implications were that a larger loading dose for the intravenous regimen should be considered; where feasible, individualised dosing of magnesium sulphate would reduce the variability in serum concentrations and might result in more women with clinically effective magnesium concentrations; and lower dose magnesium suphate regimens should be considered with caution.

The industry-wide biopharmaceutical (i.e., biologic, biotherapeutic) pipeline has been growing at an astonishing rate over the last decade with the proportion of approved new biological entities to new chemical entities on the rise. As biopharmaceuticals appear to be growing in complexity in terms of their structure and mechanism of action, so are interpretation, analysis, and prediction of their quantitative pharmacology. We present here a modeling and simulation (M&S) framework for the successful preclinical development of monoclonal antibodies (as an illustrative example of biopharmaceuticals) and discuss M&S strategies for its implementation. Critical activities during early discovery, lead optimization, and the selection of starting doses for the first-in-human study are discussed in the context of pharmacokinetic–pharmacodynamic (PKPD) and M&S. It was shown that these stages of preclinical development are and should be reliant on M&S activities including systems biology (SB), systems pharmacology (SP), and translational pharmacology (TP). SB, SP, and TP provide an integrated and rationalized framework for decision making during the preclinical development phase. In addition, they provide increased target and systems understanding, describe and interpret data generated in vitro and in vivo, predict human PKPD, and provide a rationalized approach to designing the first-in-human study.

Purpose: The translation of nonclinical oncology studies is a subject of continuous debate. We propose that translational oncology studies need to optimize both pharmacokinetic (drug exposure) and pharmacodynamic (xenograft model) aspects. While improvements in pharmacodynamic translatability can be obtained by choosing cell lines or patient-derived xenograft models closer to the clinical indication, significant ambiguity and variability exists when optimizing the pharmacokinetic translation of small molecule and biotherapeutic agents. Experimental Design and Results: In this work, we propose a pharmacokinetic-based strategy to select nonclinical doses for approved drug molecules. We define a clinically relevant dose (CRD) as the dosing regimen in mice that most closely approximates the relevant pharmacokinetic metric in humans. Such metrics include area under the time–concentration curve and maximal or minimal concentrations within the dosing interval. The methodology is applied to six drugs, including targeted agents and chemotherapeutics, small and large molecules (erlotinib, dasatinib, vismodegib, trastuzumab, irinotecan, and capecitabine). The resulting efficacy response at the CRD is compared with clinical responses. Conclusions: We conclude that nonclinical studies designed with the appropriate CRDs of approved drug molecules will maximize the translatability of efficacy results, which is critical when testing approved and investigational agents in combination. Clin Cancer Res; 23(4); 1080–90. ©2016 AACR.

A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multiscale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multiscale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions to guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multiscale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical and computational techniques employed for multiscale modeling approaches used in pharmacometric and systems pharmacology models in drug development and present several examples illustrating the current state-of-the-art models for (i) excitable systems and applications in cardiac disease; (ii) stem cell driven complex biosystems; (iii) nanoparticle delivery, with applications to angiogenesis and cancer therapy; (iv) host-pathogen interactions and their use in metabolic disorders, inflammation and sepsis; and (v) computer-aided design of nanomedical systems. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multiscale models.

Mathematical models of drug action are essential in contemporary drug discovery and development. Applications include exposure-response modeling (pharmacokinetics-pharmacodynamics or PK-PD); quantitative understanding of biological target and pathway; and systems approaches that integrate characteristics of the biological system with associated drug exposure. Encompassing empirical, mechanistic, or semi-mechanistic approaches, these mathematical models are informed by experimental data quantifying not only drug exposure (pharmacokinetics) and associated biological response (biomarkers), but also system-specific parameters intermediate between drug exposure and response. These system-specific endpoints, or biomeasures, include target-specific measurements such as density, turnover, shedding, and internalization rate. Quantifying these pharmacokinetic and pharmacodynamic endpoints—which include small molecule, biological, and cellular measures—requires a diverse repertoire of analytical instrumentation and approaches. The discipline partnership between quantitative bioanalytics and systems modeling provides an invaluable tool to improve the success of pharmaceutical research and development. The authors will provide a perspective on the interface between laboratory science and mathematical modeling to improve assessment of exposure-response relationships, and ultimately successful drug development.

PF04942847 [2-amino-4-{4-chloro-2-[2-(4-fluoro-1H-pyrazol-1-yl)ethoxy]-6-methylphenyl}-N-(2,2-difluoropropyl)-5,7-dihydro-6H-pyrrolo[3,4-d]pyrimidine-6-carboxamide] was identified as an orally available, ATP-competitive, small-molecule inhibitor of heat shock protein 90 (HSP90). The objectives of the present study were: 1) to characterize the pharmacokinetic-pharmacodynamic relationship of the plasma concentrations of PF04942847 to the inhibition of HSP90-dependent protein kinase, AKT, as a biomarker and 2) to characterize the relationship of AKT degradation to tumor growth inhibition as a pharmacological response (antitumor efficacy). Athymic mice implanted with MDA-MB-231 human breast cancer cells were treated with PF04942847 once daily at doses selected to encompass ED(50) values. Plasma concentrations of PF04942847 were adequately described by a two-compartment pharmacokinetic model. A time delay (hysteresis) was observed between the plasma concentrations of PF04942847 and AKT degradation; therefore, a link model was used to account for the hysteresis. The model reasonably fit the time courses of AKT degradation with the estimated EC(50) of 18 ng/ml. For tumor growth inhibition, the signal transduction model reasonably fit the inhibition of individual tumor growth curves with the estimated EC(50) of 7.3 ng/ml. Thus, the EC(50) for AKT degradation approximately corresponded to the EC(50) to EC(80) for tumor growth inhibition, suggesting that 50% AKT degradation was required for significant antitumor efficacy (50-80%). The consistent relationship between AKT degradation and antitumor efficacy was also demonstrated by applying an integrated signal transduction model for linking AKT degradation to tumor growth inhibition. The present results will be helpful in determining the appropriate dosing regimen and guiding dose escalation to achieve efficacious systemic exposure in the clinic.

Objective: We determined the postoperative pharmacokinetics (PK), safety, and analgesic effects of ketorolac in 14 infants (aged

Therapeutic drug monitoring of daily intravenous (IV) busulfan currently requires hospital admission. Population pharmacokinetic modeling and determination of an optimal pharmacokinetic sampling schedule over 6 hours could allow for personalizing these busulfan doses in the outpatient clinic. A retrospective evaluation of daily IV busulfan pharmacokinetics was conducted in 37 adults. SPK and NONMEM software were used to estimate the population pharmacokinetic parameters. Subsequent to model building, the area under the concentration-time curve (AUC) was computed using NONMEM. A 1-compartment model best fit the data. The optimal 6-hour outpatient sampling schedule was constructed using a simulation approach that sought to minimize scaled mean squared error for the clearance and volume parameters for each simulated individual. The best sampling times were 2.75, 3, 3.25, 5.5, 5.75, and 6 hours from the start of a 3-hour infusion. With these sampling times, the maximum a posteriori (MAP) Bayesian estimation was superior to maximum likelihood estimation with more samples. An individual patient's busulfan AUC and pharmacokinetic parameters may be accurately estimated with an outpatient sampling schedule that is used in conjunction with MAP Bayesian estimation, with a parameter prior based on population pharmacokinetic modeling. Prospective validation of this approach is needed.

Nurses caring for traumatic brain injury (TBI) patients with intracranial hypertension (ICH) recognize that patients whose intracranial adaptive capacity is reduced are susceptible to periods of disproportionate increase in intracranial pressure (DIICP) in response to a variety of stimuli. It is possible that DIICP signals potential secondary brain damage due to sustained or intermittent ICH. However, there are few clinically accessible intracranial pressure (ICP) measurement parameters that allow nurses and other critical care clinicians to identify patients at risk of DIICP. The purpose of this study was to investigate whether there are specific minute-to-minute trends in ICP variability during the first 48 hr of monitoring that might accurately predict DIICP in patients with severe TBI. A total of 38 patients with severe TBI were sampled from the data set of a randomized controlled trial testing bedside monitoring displays and cerebral perfusion pressure management in individuals with TBI or sub-arachnoid hemorrhage. The investigators retrospectively examined the rates of change (slope) in mean, standard deviation, and variance of ICP on a 1-min basis for 30 consecutive min prior to a specified DIICP event. There was a significantly increasing linear and quadratic slope in mean ICP prior to the development of DIICP, compared with the comparison data set (p < .05). It is feasible to display moving averages in modern bedside monitoring. Such an arrangement may be useful to provide visual displays that provide immediate clinically relevant information regarding the patients with decreased adaptive capacity and therefore increased risk of DIICP.

We describe a method to measure protein synthesis and catabolism in humans without prior purification and use the method to measure the turnover of surfactant protein-B (SP-B). SP-B, a lung-specific, hydrophobic protein essential for fetal-neonatal respiratory transition, is present in only picomolar quantities in tracheal aspirate samples and difficult to isolate for dynamic turnover studies using traditional in vivo tracer techniques. Using infusion of [5,5,5-(2)H(3)] leucine and a targeted proteomics method, we measured both the quantity and kinetics of SP-B tryptic peptides in tracheal aspirate samples of symptomatic newborn infants. The fractional synthetic rate (FSR) of SP-B measured using the most abundant proteolytic fragment, a 10 amino acid peptide from the carboxy-terminus of proSP-B (SPTGEWLPR), from the circulating leucine pool was 0.035 +/- 0.005 h(-1), and the fractional catabolic rate was 0.044 +/- 0.003 h(-1). This technique permits high-throughput and sensitive measurement of turnover of low abundance proteins with minimal sample preparation.

In this paper, we examined the added value of fuzzy nonlinear regression to identify individual pharmacokinetic parameters in the case of noisy fuzzy data and/or small sample sizes. We first described three approaches that use least squares of errors as a fitting criterion for parameter estimation by fuzzy regression. Next, we compared the estimation and prediction capability of fuzzy least squares (FLS) and ordinary least squares (OLS) regressions via a simulation experiment, so as to determine the conditions of data size and variability under which one approach could be deemed superior over the other. We considered two empirical pharmacokinetic models. Our results showed that OLS regression outperformed FLS regression when the sample size was larger and/or there existed more outliers in the data. Overall, FLS regression was more powerful as the dataset size decreased. When the data were smaller in size and contained more variability, FLS regression's performance remained better than that of the OLS regression. Although the accuracy of the three FLS regression approaches was very close in almost all instances, those that estimated fuzzy parameters were superior in terms of predictive capability. These findings could aid in selecting the proper regression technique to employ in the presence of fuzzy data.

Developing a population-based pharmacokinetic-pharmacodynamic (PKPD) model is a challenge in ophthalmology due to the difficulty of obtaining adequate pharmacokinetic (PK) samples from ocular tissues to inform the pharmacodynamic (PD) model. Using limited PK data, we developed a preclinical population-based PD model suitable for capturing the time course of dog intraocular pressure (IOP) that exhibited time-dependent sensitization after topical administration of PF-04475270 [5-{3-[(2S)-2-{(3R)-3-hydroxy-4-[3-(trifluoromethyl)phenyl]butyl}-5-oxopyrrolidin-1-yl]propyl}thiophene-2-carboxylate]. A physiologically relevant PK model was chosen to simultaneously capture the concentration profiles of CP-734432, a potent EP4 agonist and the active metabolite of PF-04475270, sampled from three ocular tissues of the anterior chamber: cornea, aqueous humor, and iris-ciliary body. Two population-based PD models were developed to characterize the IOP lowering profiles: model I, a standard indirect-response model (IRM); and model II, an extension of a standard IRM that empirically incorporated a response-driven positive feedback loop to account for the observed PD sensitization. The PK model reasonably described the PK profiles in all three ocular tissues. As for the PD, model I failed to capture the overall trend in the population IOP data, and model II more adequately characterized the overall data set. This integrated PKPD model may have general utility when PD sensitization is observed and is not a result of time-dependent PK. In addition, the model is applicable in the ophthalmology drug development setting in which PK information is limited but a population-based PD model could reasonably be established.

Purpose: Fludarabine monophosphate (fludarabine) is frequently administered to patients receiving a reduced-intensity conditioning regimen for allogeneic hematopoietic cell transplant (HCT) in an ambulatory care setting. These patients experience significant interpatient variability in clinical outcomes, potentially due to pharmacokinetic variability in 2-fluoroadenine (F-ara-A) plasma concentrations. To test such hypotheses, patient compliance with the blood sampling should be optimized by the development of a minimally intrusive limited sampling schedule (LSS) to characterize F-ara-A pharmacokinetics. To this end, we sought to create the first F-ara-A population pharmacokinetic model and subsequently a LSS. Experimental Design: A retrospective evaluation of F-ara-A pharmacokinetics was conducted after one or more doses of daily i.v. fludarabine in 42 adult HCT recipients. NONMEM software was used to estimate the population pharmacokinetic parameters and compute the area under the concentration-time curve. Results: A two-compartment model best fits the data. A LSS was constructed using a simulation approach, seeking to minimize the scaled mean squared error for the area under the concentration-time curve for each simulated individual. The LSS times chosen were 0.583, 1.5, 6.5, and 24 hours after the start of the 30-minute fludarabine infusion. Discussion: The pharmacokinetics of F-ara-A in an individual HCT patient can be accurately estimated by obtaining four blood samples (using the LSS) and maximum a posteriori Bayesian estimation. Conclusion: These are essential tools for prospective pharmacodynamic studies seeking to determine if clinical outcomes are related to F-ara-A pharmacokinetics in patients receiving i.v. fludarabine in the ambulatory clinic. (Clin Cancer Res 2009;15(16):5280–7)

This chapter describes the System for Population Kinetics (SPK), a novel Web service for performing population kinetic analysis. Population kinetic analysis is a widely-used tool for extracting information about the probability distributions of unknown parameters in kinetic models. The statistical population model is usually hierarchical, with a nested structure encompassing both variation between subjects and residual unexplained variation associated with the model predictions. The complexity of the analysis is largely driven by the nonlinearity of the models employed. Here, we provide a concise introduction to the topic and a historical perspective for the benefit of the reader who is new to these concepts. Next, we briefly describe the SPK open source system and its multi-tiered architecture, indicating the user goals it set to achieve and elucidating its practical usage with examples.

Cyclophosphamide-based regimens are front-line treatment for numerous pediatric malignancies; however, current dosing methods result in considerable interpatient variability in tumor response and toxicity. In this pediatric population, the authors' objectives were (1) to quantify and explain the pharmacokinetic variability of cyclophosphamide and 2 of its metabolites, hydroxycyclophosphamide (HCY) and carboxyethylphosphoramide mustard (CEPM), and (2) to apply a population pharmacokinetic model to describe the disposition of cyclophosphamide and these metabolites. A total of 196 blood samples were obtained from 22 children with neuroblastoma receiving intravenous cyclophosphamide (400 mg/m2/d) and topotecan. Blood samples were quantitated for concentrations of cyclophosphamide, HCY, and CEPM using liquid chromatography-mass spectrometry and analyzed using nonlinear mixed-effects modeling with the NONMEM software system. After model building was complete, the area under the concentration-time curve (AUC) was computed using NONMEM. Cyclophosphamide elimination was described by noninducible and inducible routes, with the latter producing HCY. Glomerular filtration rate was a covariate for the fractional elimination of HCY and its conversion to CEPM. Considerable interpatient variability was observed in the AUC of cyclophosphamide, HCY, and CEPM. These results represent a critical first step in developing pharmacokinetic-linked pharmacodynamic studies in children receiving cyclophosphamide to determine the clinical relevance of the pharmacokinetic variability in cyclophosphamide and its metabolites.

Background Intracranial hypertension due to primary and secondary injuries is a prime concern when providing care to patients with severe traumatic brain injury. Increases in intracranial pressure vary depending on compensatory processes within the craniospinal space, also referred to as intracranial adaptive capacity. In patients with traumatic brain injury and decreased intracranial adaptive capacity, intracranial pressure increases disproportionately in response to a variety of stimuli. However, no well-validated measures are available in clinical practice to predict the development of such an increase.Objectives To examine whether P2 elevation, quantified by determining the P2:P1 ratio (=0.8) of the intracranial pressure pulse waveform, is a unique predictor of disproportionate increases in intracranial pressure on a beat-by-beat basis in the 30 minutes preceding the elevation in patients with severe traumatic brain injury, within 48 hours after deployment of an intracranial pressure monitor.Methods A total of 38 patients with severe traumatic brain injury were sampled from a randomized controlled trial of cerebral perfusion pressure management in patients with traumatic brain injury or subarachnoid hemorrhage.Results The P2 elevation was not only present before the disproportionate increase in pressure, but also appeared in the comparison data set (within-subject without such a pressure increase).Conclusions P2 elevation is not a reliable clinical indicator to predict an impending disproportionate increase in intracranial pressure.

In this review, we trace the origins of mathematical modeling methods and pay particular attention to radiotracer applications. Nuclear medicine has been advanced greatly by the efforts of the Society of Nuclear Medicine's Medical Internal Radiation Dose Committee. Well-developed mathematical methods and tools have been created in support of a wide range of applications. Applications of mathematical modeling extend well beyond biology and medicine and are essential to analysis is a wide range of fields that rely on numerical predictions, eg, weather, economic, and various gaming applications. We start with the discovery of radioactivity and radioactive transformations and illustrate selected applications in biology, physiology, and pharmacology. We discuss compartment models as tools used to frame the context of specific problems. A definition of terms, methods, and examples of particular problems follows. We present models of different applications with varying complexity depending on the features of the particular system and function being analyzed. Commonly used analysis tools and methods are described, followed by established models which describe dosimetry along gastrointestinal and urinary excretory pathways, ending finally with a brief discussion of bone marrow dose. We conclude pointing to more recent, promising methods, not yet widely used in dosimetry applications, which aim at coupling pharmacokinetic data with other patient data to correlate patient outcome (benefits and risk) with the type, amount, kind and timing of the therapy the patient received.

Probabilistic methods are insufficient for dealing with the vagueness inherent in human judgment of minimal data available during early drug development. We sought to use fuzzy set theory as a basis for quantifying and propagating vague judgment in a physiologically based pharmacokinetic (PBPK) model for diazepam disposition.First, using diazepam distribution data in rat tissues and fuzzy regression, we estimated fuzzy rat tissue-to-plasma partition coefficients (Kp's). We scaled the coefficients prior to human PBPK modeling. Next, we constructed the fuzzy set of hepatic intrinsic clearance (CLint) by integrating CLint values measured in vitro from human hepatocytes. Finally, we used these parameters, and other physiological and biochemical information, to predict human diazepam disposition. We compared the simulated plasma kinetics with published concentration-time profiles.We successfully identified rat Kp's by fuzzy regression. The predicted rat tissue concentration-time contours enveloped the animal tissue distribution data. For the human PBPK model, the mean in vivo plasma concentrations were contained in the simulated concentration-time envelopes.We present a novel computational approach for handling information paucity in PBPK models using fuzzy arithmetic. Our methodology can model the vagueness associated with human perception and interpretation of minimal drug discovery data.

In recent years, development of rheumatoid arthritis (RA) drug therapy has been more directly targeted to counteract specific mechanisms of inflammation, and it is now believed that early aggressive treatment with disease modifying drugs is important to inhibit future structural joint damage. The development of these new treatments has increased the need for methodologies to assess disease activity in RA and monitor the effectiveness of drug therapy. Unlike X-ray, which shows only structural bone damage, magnetic resonance imaging (MRI) can depict soft tissue damage and synovitis, the primary pathology of RA. Recent studies have also indicated that MRI is sensitive to pathophysiologic changes that may predate radiographic erosions and may predict future joint damage. In this study, we have developed a computer automated analysis technique for MR wrist images that provides an objective measure of RA synovitis. This method applies a two-compartment pharmacokinetic model to every voxel of a dynamic contrast-enhanced MRI (DCE-MRI) dataset and outputs resulting parametric images. The aim of this technique is to not only objectively quantify the severity of rheumatoid synovitis, but to also locally determine where areas of serious disease activity are situated through kinetic modeling of blood-tissue exchange. Preliminary results show good correlation to early enhancement rate, which has previously been shown to be a useful clinical marker of RA activity. However, the use of tracer kinetic modeling methods potentially provides more specific information regarding underlying RA physiology. This approach could provide a useful new tool in RA patient management and could substantially improve RA therapeutic studies by calculating objective biomarkers of the disease state.

Amoxicillin is recommended for anthrax prevention in pregnancy. The objective of this study was to evaluate the pharmacokinetics of amoxicillin during pregnancy and postpartum (PP). Sixteen women received amoxicillin during gestation (18-22 weeks (T2) and 30-34 weeks (T3)) as well as 3 months postpartum (PP) to evaluate single-dose pharmacokinetics. Amoxicillin compartmental pharmacokinetic parameters were used to simulate amoxicillin concentration-time profiles following different dosage strategies. Amoxicillin CL(renal) (T2: 24.8+/-6.7 l/h, P

The Thyrotoxicosis Therapy Follow-up Study (TTFUS) is comprised of 35,593 hyperthyroid patients treated from the mid-1940s through the mid-1960s. One objective of the TTFUS was to evaluate the long-term effects of high-dose iodine-131 ((131)I) treatment (1-4). In the TTFUS cohort, 23,020 patients were treated with (131)I, including 21,536 patients with Graves disease (GD), 1,203 patients with toxic nodular goiter (TNG) and 281 patients with unknown disease. The study population constituted the largest group of hyperthyroid patients ever examined in a single health risk study. The average number (± 1 standard deviation) of (131)I treatments per patient was 1.7 ± 1.4 for the GD patients and 2.1 ± 2.1 for the TNG patients. The average total (131)I administered activity was 380 ± 360 MBq for GD patients and 640 ± 550 MBq for TNG patients. In this work, a biokinetic model for iodine was developed to derive organ residence times and to reconstruct the radiation-absorbed doses to the thyroid gland and to other organs resulting from administration of (131)I to hyperthyroid patients. Based on (131)I data for a small, kinetically well-characterized sub-cohort of patients, multivariate regression equations were developed to relate the numbers of disintegrations of (131)I in more than 50 organs and tissues to anatomical (thyroid mass) and clinical (percentage thyroid uptake and pulse rate) parameters. These equations were then applied to estimate the numbers of (131)I disintegrations in the organs and tissues of all other hyperthyroid patients in the TTFUS who were treated with (131)I. The reference voxel phantoms adopted by the International Commission on Radiological Protection (ICRP) were then used to calculate the absorbed doses in more than 20 organs and tissues of the body. As expected, the absorbed doses were found to be highest in the thyroid (arithmetic means of 120 and 140 Gy for GD and TNG patients, respectively). Absorbed doses in organs other than the thyroid were much smaller, with arithmetic means of 1.6 Gy, 1.5 Gy and 0.65 Gy for esophagus, thymus and salivary glands, respectively. The arithmetic mean doses to all other organs and tissues were more than 100 times less than those to the thyroid gland. To our knowledge, this work represents the most comprehensive study to date of the doses received by persons treated with (131)I for hyperthyroidism.

The purpose of this commentary is to place probability of trial success, or assurance, in the context of decision making in drug development, and to illustrate its properties in an intuitive manner for the readers of Clinical Pharmacology and Therapeutics. The hope is that this will stimulate a dialog on how assurance should be incorporated into a quantitative decision approach for clinical development and trial design that uses all available information.

In drug development, in vivo assessment of target engagement provides confidence when testing the drug's mechanism of action and improves the likelihood of clinical success. For biologics, receptor occupancy (RO) determined from circulating cells can provide evidence of target engagement. Integrating this information with mathematical modeling can further enhance the understanding of drug-target interactions and the biological factors that are critical to the successful modulation of the target and ultimately the disease state.This mini-review presents two specific types of mathematical models used to describe antibody-receptor systems and highlights how experimental data can inform the model parameters. Simulations are used to illustrate how various mechanisms influence RO, PK and total cellular receptor profiles.The simulations demonstrate the effect antibody-receptor internalization, affinity and receptor turnover have on commonly acquired data in drug development.Integrating RO data with mathematical models such as the two presented here (target-mediated drug disposition and site-of-action models) can provide a more comprehensive view of the biological system, which can be used to test hypotheses, extrapolate preclinical findings to humans and impact clinical study designs and risk assessments for the successful development of biotherapeutics.

Therapeutic protein products (TPPs) are of considerable value in the treatment of a variety of diseases, including cancer, hemophilia, and autoimmune diseases. The success of TPP mainly results from prolonged half-life, increased target specificity and decreased intrinsic toxicity compared with small molecule drugs. However, unwanted immune responses against TPP, such as generation of anti-drug antibody, can impact both drug efficacy and patient safety, which has led to requirements for increased monitoring in regulatory studies and clinical practice, termination of drug development, or even withdrawal of marketed products. We present an overview of current knowledge on immunogenicity of TPP and its impact on efficacy and safety. We also discuss methods for measurement and prediction of immunogenicity and review both product-related and patient-related risk factors that affect its development, and efforts that may be taken to mitigate it. Lastly, we discuss gaps in knowledge and technology and what is needed to fill these.

The intravenous glucose tolerance test (IVGTT) interpreted with the minimal model provides individual indexes of insulin sensitivity (SI) and glucose effectiveness (SG). In population studies, the traditional approach, the standard two-stage (STS) method, fails to account for uncertainty in individual estimates, resulting in an overestimation of between-subject variability. Furthermore, in the presence of reduced sampling and/or insulin resistance, individual estimates may be unobtainable, biasing population information. Therefore, we investigated the use of two population approaches, the iterative two-stage (ITS) method and nonlinear mixed-effects modeling (NM), in a population ( n = 235) of insulin-sensitive and insulin-resistant subjects under full (FSS, 33 samples) and reduced [RSS(240-min), 13 samples and RSS(180-min), 12 samples] IVGTT sampling schedules. All three population methods gave similar results with the FSS. Using RSS(240), the three methods gave similar results for SI, but SG population means were overestimated. With RSS(180), SI and SG population means were higher for all three methods compared with their FSS counterparts. NM estimated similar between-subject variability (19% SG, 53% SI) with RSS(180), whereas ITS showed regression to the mean for SG (0.01% SG, 56% SI) and STS provided larger population variability in SI (29% SG, 91% SI). NM provided individual estimates for all subjects, whereas the two-stage methods failed in 16–18% of the subjects using RSS(180) and 6–14% using RSS(240). We conclude that population approaches, specifically NM, are useful in studies with a sparsely sampled IVGTT (∼12 samples) of short duration (∼3 h) and when individual parameter estimates in all subjects are desired.

Purpose: Dose-related toxicity of cyclophosphamide may be reduced and therapeutic efficacy may be improved by pharmacokinetic sampling and dose adjustment to achieve a target area under the curve (AUC) for two of its metabolites, hydroxycyclophosphamide (HCY) and carboxyethylphosphoramide mustard (CEPM). To facilitate real-time dose adjustment, we developed open-source code within the statistical software R that incorporates individual data into a population pharmacokinetic model.Experimental Design: Dosage prediction performance was compared to that obtained with nonlinear mixed-effects modeling using NONMEM in 20 cancer patients receiving cyclophosphamide. Bayesian estimation of individual pharmacokinetic parameters was accomplished from limited (i.e., five samples over 0-16 hours) sampling of plasma HCY and CEPM after the initial cyclophosphamide dose. Conditional on individual pharmacokinetics, simulations of the AUC of both HCY and CEPM were provided for a range of second doses (i.e., 0-100 mg/kg cyclophosphamide).Results: The results compared favorably with NONMEM and returned accurate predictions for AUCs of HCY and CEPM with comparable mean absolute prediction error and root mean square prediction error. With our method, the mean absolute prediction error and root mean square prediction error of AUC CEPM were 11.0% and 12.8% and AUC HCY were 31.7% and 44.8%, respectively.Conclusions: We developed dose adjustment software that potentially can be used to adjust cyclophosphamide dosing in a clinical setting, thus expanding the opportunity for pharmacokinetic individualization of cyclophosphamide. The software is simple to use (requiring no programming experience), reads individual patient data directly from an Excel spreadsheet, and runs in less than 5 minutes on a desktop PC.

High shear enhances the adhesion of Escherichia coli bacteria binding to mannose coated surfaces via the adhesin FimH, raising the question as to whether FimH forms catch bonds that are stronger under tensile mechanical force. Here, we study the length of time that E. coli pause on mannosylated surfaces and report a double exponential decay in the duration of the pauses. This double exponential decay is unlike previous single molecule or whole cell data for other catch bonds, and indicates the existence of two distinct conformational states. We present a mathematical model, derived from the common notion of chemical allostery, which describes the lifetime of a catch bond in which mechanical force regulates the transitions between two conformational states that have different unbinding rates. The model explains these characteristics of the data: a double exponential decay, an increase in both the likelihood and lifetime of the high-binding state with shear stress, and a biphasic effect of force on detachment rates. The model parameters estimated from the data are consistent with the force-induced structural changes shown earlier in FimH. This strongly suggests that FimH forms allosteric catch bonds. The model advances our understanding of both catch bonds and the role of allostery in regulating protein activity.

Hemostasis in humans and other animals is a complex process that controls blood loss after a vascular injury. Factor XIII (FXIII) stabilizes clots primarily by cross-linking fibrin, thus protecting a newly formed clot from fibrinolysis by plasmin. Congenital deficiencies in humans involving FXIII are associated with delayed bleeding and wound healing and severe spontaneous hemorrhaging. These symptoms can be alleviated by intravenous administration of enriched FXIII plasma fractions. Circulating plasma FXIII is found as a heterotetramer that dissociates in the presence of calcium and thrombin into an active dimer and 2 inactive monomers. The recombinant FXIII under investigation is the active dimer alone. A 3-compartment, nonlinear population pharmacokinetic model was implemented in NONMEM V and then used to analyze data from preclinical studies in cynomolgus monkeys. The model simultaneously describes endogenous production of dimer (0.622 microg kg(-1) hr(-1)) and monomer (12.1 microg kg(-1) hr(-1)), and the administration of recombinant dimer. The model incorporates the rate and extent of complexation of recombinant dimer with available endogenous monomer (6.59 mg(-1) kg hr(-1)) to form the heterotetramer. Half-lives for dimer, heterotetramer, and monomer (3.33 hours, 2.83 days, and 3.94 hours for A(2), A(2)B(2), and B, respectively) were estimated, along with their variability in the population studied.

When cyclophosphamide (120 mg/kg) is used for hematopoietic cell transplant, the increased area under the curve of carboxyethylphosphoramide mustard (AUCCEPM) is related to liver toxicity and death. We determined the feasibility of dose-adjusting cyclophosphamide to a preset metabolic endpoint (AUCCEPM, 325 ± 25 μmol/L · h). In 20 patients blood sampling was done over a 16-hour period after administration of 45 mg/kg cyclophosphamide; AUCCEPM from 0 to 16 hours was calculated by noncompartmental analysis. The expected AUCCEPM for 0 to 48 hours was estimated, and the second cyclophosphamide dose was determined. The mean second cyclophosphamide dose was 42 mg/kg, and the mean total cyclophosphamide dose was 86 mg/kg (range, 54–120 mg/kg). The mean AUCCEPM for the time from 0 to 48 hours was 296 μmol/L · h (95% confidence interval, 275–317 μmol/L · h). A retrospective analysis indicated that AUCCEPM could be more accurately predicted by use of a population pharmacokinetic model. We conclude that metabolism-based dosing of cyclophosphamide is feasible and that a lower cyclophosphamide dose does not affect engraftment. Clinical Pharmacology & Therapeutics (2005) 78, 298–308; doi: 10.1016/j.clpt.2005.05.005

A deconvolution algorithm, based on a Bayesian statistical framework and smoothing spline technique, is applied to reconstructing input functions from noisy measurements in biological systems. Deconvolution is usually ill-posed. However, placing a Bayesian prior distribution on the input function can make the problem well-posed. Using this algorithm and a computational model of diffusional oxygen transport in an approximately cylindrical muscle (about 0.5-mm diameter and 10-mm long mouse leg muscle), the time course of muscle oxygen uptake and mitochondrial oxygen consumption, both during isometric twitch contractions (at various frequencies) and the recovery period, is estimated from polarographic measurements of oxygen concentration on the muscle surface. An important feature of our experimental protocol is the availability of data for the apparatus characteristics. From these time courses, the actual mitochondrial consumption rates during resting and exercise states can be estimated. Mitochondrial oxygen consumption rate increased during stimulation to a maximum steady state value approximately five times of the resting value of 0.63 nmol/s/g wet weight for the stimulation conditions studied. Diffusion slowed the kinetic responses to the contraction but not the steady state fluxes during the stimulation interval.

Advances in computer hardware and the associated computer-intensive algorithms made feasible by these advances [like Markov chain Monte Carlo (MCMC) data analysis techniques] have made possible the application of hierarchical full Bayesian methods in analyzing pharmacokinetic and pharmacodynamic (PK-PD) data sets that are multivariate in nature. Pharmacokinetic data analysis in particular has been one area that has seized upon this technology to refine estimates of drug parameters from sparse data gathered in a large, highly variable population of patients. A drawback in this type of analysis is that it is difficult to quantitatively assess convergence of the Markov chains to a target distribution, and thus, it is sometimes difficult to assess the reliability of estimates gained from this procedure. Another complicating factor is that, although the application of MCMC methods to population PK-PD problems has been facilitated by new software designed for the PK-PD domain (specifically PKBUGS), experts in PK-PD may not have the necessary experience with MCMC methods to detect and understand problems with model convergence. The objective of this work is to provide an example of a set of diagnostics useful to investigators, by analyzing in detail three convergence criteria (namely the Raftery and Lewis, Geweke, and Heidelberger and Welch methods) on a simulated problem and with a rule of thumb of 10,000 chain elements in the Markov chain. We used two publicly available software packages to assess convergence of MCMC parameter estimates; the first performs Bayesian parameter estimation (PKBUGS/WinBUGS), and the second is focused on posterior analysis of estimates (BOA). The main message that seems to emerge is that accurately estimating confidence regions for the parameters of interest is more demanding than estimating the parameter means. Together, these tools provide numerical means by which an investigator can establish confidence in convergence and thus in the estimated parameters derived from hierarchical full Bayesian pharmacokinetic data analysis.

Background: Homeostasis model assessment (HOMA) and quantitative insulin-sensitivity check index (QUICKI) are measures of insulin resistance and insulin sensitivity derived from fasting glucose (FG) and insulin levels. They thus should reflect, in principle, insulin action on both the liver and the periphery. Methods: Twenty-three prepubertal and early pubertal adolescents were studied at baseline and after 6 months, using the frequently sampled intravenous glucose tolerance test (IVGTT) with labeled glucose. Total body insulin sensitivity (SI) was calculated using the minimal model and total glucose concentrations. Peripheral insulin sensitivity (SI*) was calculated from labeled glucose concentrations. Hepatic insulin resistance (HIR) was calculated by multiplying glucose production over the last hour by the average insulin level. HOMA and QUICKI were calculated from the fasting glucose and insulin values. Results: HOMA, QUICKI fasting insulin, and glucose to insulin ratio were all significantly related to SI (p

A detailed pharmacokinetic analysis was performed with 47 children from Papua New Guinea with uncomplicated falciparum or vivax malaria treated with artesunate (ARTS) suppositories (Rectocaps) given in two doses of approximately 13 mg/kg of body weight 12 h apart. Following an intensive sampling protocol, samples were assayed for ARTS and its primary active metabolite, dihydroartemisinin (DHA), by liquid chromatography-mass spectrometry. A population pharmacokinetic model was developed to describe the data. Following administration of the first dose, the mean maximal concentrations of ARTS and DHA were 1,085 nmol/liter at 0.9 h and 2,525 nmol/liter at 2.3 h, respectively. The absorption half-life for ARTS was 2.3 h, and the conversion half-life (ARTS to DHA) was 0.27 h, while the elimination half-life of DHA was 0.71 h. The mean common volumes of distribution for ARTS and DHA relative to bioavailability were 42.8 and 2.04 liters/kg, respectively, and the mean clearance values relative to bioavailability were 6 and 2.2 liters/h/kg for ARTS and DHA, respectively. Substantial interpatient variability was observed, and the bioavailability of the second dose relative to that of the first was estimated to be 0.72. The covariates age, sex, and α-thalassemia genotype were not influential in the pharmacokinetic model development; but the inclusion of weight as a covariate significantly improved the performance of the model. An ARTS suppositories dose of 10 of 20 mg/kg is appropriate for use in children with uncomplicated malaria.

Background— Neovasculature within atherosclerotic plaques is believed to be associated with infiltration of inflammatory cells and plaque destabilization. The aim of the present investigation was to determine whether the amount of neovasculature present in advanced carotid plaques can be noninvasively measured by dynamic, contrast-enhanced MRI. Methods and Results— A total of 20 consecutive patients scheduled for carotid endarterectomy were recruited to participate in an MRI study. Images were obtained at 15-second intervals, and a gadolinium contrast agent was injected coincident with the second of 10 images in the sequence. The resulting image intensity within the plaque was tracked over time, and a kinetic model was used to estimate the fractional blood volume. For validation, matched sections from subsequent endarterectomy were stained with ULEX and CD-31 antibody to highlight microvessels. Finally, all microvessels within the matched sections were identified, and their total area was computed as a fraction of the plaque area. Results were obtained from 16 participants, which showed fractional blood volumes ranging from 2% to 41%. These levels were significantly higher than the histological measurements of fractional vascular area. Nevertheless, the 2 measurements were highly correlated, with a correlation coefficient of 0.80 ( P Conclusions— Dynamic contrast-enhanced MRI provides an indication of the extent of neovasculature within carotid atherosclerotic plaque. MRI therefore provides a means for prospectively studying the link between neovasculature and plaque vulnerability.

Cancer immunotherapy focuses on stimulating and promoting the immune system to recognize and eliminate cancer cells, with several FDA approvals in recent years. However, identifying patients best suited for specific immune therapies, and determining optimal treatment regimens continue to be a clinical challenge. Using a molecular-detailed computational systems pharmacology model to identify cellular biomarkers and optimize regimens, we may be able to predict the efficacy of regimens in specific patient populations, and expedite drug development for cancer treatment. We developed a cell/receptor-based multi-compartment systems pharmacology model focusing on the immune response against a growing tumor, with the intent to test the effects of immune checkpoint inhibitors against PD-1, PD-L1 and CTLA-4 administered as mono- and combination therapies. Additionally, the model also allows for testing of other immuno-therapies, such as adoptive cell therapies, which can be combined with the checkpoint inhibitors. The model was designed and developed using the SimBiology plug-in in MATLAB. Simulations were performed with parameters that define the immune response at particular tumor stages of melanoma and NSCLC. All results were qualitatively and quantitatively compared to experimental pre-clinical and clinical data for model validation, or used for the generation of predictions suitable for further experimental testing. In silico, we have identified that administrations of the prescribed doses of 1-10 mg/kg of anti-CLTA-4 (based on binding kinetics) effectively saturates the receptors on the T cells, and promotes both an extended life span of the antigen presenting cells (APCs), and the maximum attainable activation levels of the effector T cells. The model further predicts that the effectiveness of anti-CTLA-4 therapy is limited by the immunogenicity of the system (i.e., the antigen intensity level and number of APCs presenting the antigens) in a monotonic fashion. Furthermore, injecting activated APCs without therapy would show a temporary tumor response and a subsequent recovery by the tumor to its original growth trajectory, while raising the antigen intensity had a sustained effect on tumor response. Other simulations indicate that, despite the lack of apparent tumor response, a sustained immune attack may be ongoing in the body; however, the immune activity is proportionally limited by the tumor and regulatory cells. Lastly, several dose-responses and clinical trials were simulated for both combination and monotherapies, and correlated with published clinical trial data. Future work will focus on uncovering the cellular biomarkers responsible for such results, experimentally validating them, as well as simulating optimal combination treatment regimens for future evaluation. Citation Format: Oleg Milberg, Chang Gong, Bing Wang, Paolo Vicini, Rajesh Narwal, Lorin Roskos, Aleksander Popel. Systems pharmacology to predict cellular biomarkers and optimize mono- and combination-therapy regimens: Focusing on immune checkpoint targets PD-1, PD-L1 and CTLA-4 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4531. doi:10.1158/1538-7445.AM2017-4531

When the immune system responds to tumor development, patterns of immune infiltrates emerge, highlighted by expression of immune checkpoint-related molecules such as PD-L1 on cancer cells and its receptor PD-1 on cytotoxic T cells. Pre-treatment tumor spatial heterogeneity could bear information on intrinsic characteristics of the tumor lesion for individual patient, and thus has the potential to comprise biomarkers for anti-tumor therapeutics. We developed a systems biology computational multiscale agent-based model to capture the interactions between immune cells and cancer cells during tumor progression. Cytotoxic T cells and cancer cells are modeled as free-moving agents in a 3-dimensional grid, where each cell acts in response to its local microenvironment and carries out functions such as division, apoptosis, cytotoxic killing and switching between states with different PD-1 or PD-L1 expression levels. Subsequently, we analyzed the emergent behavior of tumor progression by looking at all these local interactions as a whole. Using this model, we are able to reproduce temporal dynamics of cytotoxic T cells and cancer cells during general tumor progression, as well as 3-dimensional spatial distributions of these cells over the time course of the simulation. By varying the characteristics of the neoantigen profile of individual patients, such as mutational burden and immunogenicity, a spectrum of pre-treatment spatial patterns of PD-1/PD-L1 expression is generated in our simulations, resembling immune-architectures obtained via immunohistochemistry from patient biopsies. We evaluate potential prognostic biomarkers by correlating these spatial characteristics with in silico treatment results with immune checkpoint inhibitors. Simulation results demonstrate that the percentage of PD-L1 positive cancer cells which are not in close proximity of the tumor boundary or vasculature is more indicative of successful anti-PD1/anti-PD-L1 treatment. Our findings suggest that tumor spatial heterogeneity, especially its immune-architecture, reflects the course of tumor progression as well as patient-specific properties, and is thus likely to carry important information about tumor susceptibility to treatment such as with immune checkpoint inhibitors. We demonstrated how prognostic biomarkers could be realistically simulated in a general cancer scenario. The model is further refined for use to predict treatment/biomarker combinations in specific cancer types. Citation Format: Chang Gong, Oleg Milberg, Bing Wang, Paolo Vicini, Rajesh Narwal, Lorin Roskos, Aleksander S. Popel. A multiscale computational model for spatio-temporal tumor immune response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 975. doi:10.1158/1538-7445.AM2017-975

The orally available novel small molecules PF06463922 [(10R)-7-amino-12-fluoro-2,10,16-trimethyl-15-oxo-10,15,16,17-tetrahydro-2H-8,4-(metheno)pyrazolo[4,3-h][2,5,11]benzoxadiazacyclotetradecine-3-carbonitrile] and PF06471402 [(10R)-7-amino-12-fluoro-2,10,16-trimethyl-15-oxo-10,15,16,17-tetrahydro-2H-8,4-(azeno)pyrazolo[4,3-h][2,5,11]benzoxadiazacyclo-tetradecine-3-carbonitrile] are second-generation anaplastic lymphoma kinase (ALK) inhibitors targeted to both naive and resistant patients with non-small cell lung cancer (NSCLC) to the first-generation ALK inhibitor crizotinib. The objectives of the present study were to characterize and compare the pharmacokinetic-pharmacodynamic (PKPD) relationships of PF06463922 and PF06471402 for target modulation in tumor and antitumor efficacy in athymic mice implanted with H3122 NSCLC cells expressing a crizotinib-resistant echinoderm microtubule-associated protein-like 4 (EML4)-ALK mutation, EML4-ALK(L1196M). Furthermore, the PKPD relationships for these ALK inhibitors were evaluated and compared between oral administration and subcutaneous constant infusion (i.e., between different pharmacokinetic [PK] profiles). Oral and subcutaneous PK profiles of these ALK inhibitors were adequately described by a one-compartment PK model. An indirect response model extended with a modulator fit the time courses of PF06463922- and PF06471402-mediated target modulation (i.e., ALK phosphorylation) with an estimated unbound EC50,in vivo of 36 and 20 nM, respectively, for oral administration, and 100 and 69 nM, respectively, for subcutaneous infusion. A drug-disease model based on the turnover concept fit tumor growth curves inhibited by PF06463922 and PF06471402 with estimated unbound tumor stasis concentrations of 51 and 27 nM, respectively, for oral administration, and 116 and 70 nM, respectively, for subcutaneous infusion. Thus, the EC50,in vivo to EC60,in vivo estimates for ALK inhibition corresponded to the concentrations required tumor stasis in all cases, suggesting that the pharmacodynamic relationships of target modulation to antitumor efficacy were consistent among the ALK inhibitors, even when the PK profiles with different administration routes were considerably different.

PURPOSE: The intraocular pharmacodynamics of PF-04523655, a small-interfering RNA (siRNA) directed against RTP801, was characterized using rat models of retinopathy. METHODS: Rat models of streptozotocin-induced diabetes and wet AMD were used to determine the onset, extent, and duration of siRNA inhibition of retinal RTP801 expression by PF-04523655, and this inhibition was characterized by pharmacokinetic/pharmacodynamic (PK/PD) modeling. A rat model of wet AMD was also used to examine PF-04523655 dose-dependent effects on the incidence of clinical grade 3 or 4 choroidal neovascularization lesions. Whole homogenate versus laser-capture microdissected (LCM) retinal samples were analyzed by quantitative PCR for RTP801 expression. RESULTS: RTP801 expression in RPE/choroid (RPE/C) increased in diabetic rats by up to 70% above nondiabetic rat levels. Inhibition of retinal RTP801 expression by PF-04523655 began 1 day after intravitreous injection and was observed through day 7 in the neurosensory retina and through day 14 or longer in RPE/C. PF-04523655 inhibition of RTP801 expression was maintained well after clearance of PF-04523655 from the eye and was best characterized by an effect compartment PK/PD model. Moreover, PF-04523655 administration decreased the incidence of clinical grade 3 or 4 lesions by approximately 60% (P = 0.053), and dose-dependently inhibited retinal RTP801 expression (P < 0.01). RTP801 expression was enriched in the outer nuclear layer in LCM samples. CONCLUSIONS: In rodent retinopathy models, administration of the siRNA, PF-04523655, reduced RTP801 expression in the retina, consistent with the RNA-induced silencing complex (RISC) mechanism of action. The pharmacodynamic profile from the animal models could be useful to elucidate dose and exposure dependency of RTP801 expression inhibition by siRNA.

This chapter provides a description of blood–tissue exchange at the organ level, in particular the elementary steps of transcapillary exchange, transport through the cell membrane, and intracellular metabolism. It is shown that the most suitable experimental technique for quantitation is the multiple indicator dilution technique, which provides tracer data that requires interpretation using model-based techniques. The most important blood–tissue exchange models are distributed parameter models, although in some circumstances lumped, compartmental approaches may be adopted. The two major distributed modelling strategies are reviewed in detail. Issues related to model identification are discussed and a number of applications are outlined.

In Chapter 5, the parameter estimation problem in a single individual is discussed. In that context, the attention is focused on the mathematical formulation of the relationship between a given experimental protocol and the resulting measurements, and on the derivation of a suitable set of values for the model parameters. However, sometimes, we do not have enough measurements from a single individual to successfully perform parameter estimation and/or we are interested in modelling both the behavior of a typical subject and the variability within the population. This chapter presents a review of the different approaches to the population modelling and parameter estimation problem, focusing the reader’s attention also on the techniques related to model evaluation and model selection, as performed in the population modelling context.

To assess the serum and lower respiratory tract tobramycin concentrations (C(T)) produced by a single dose of tobramycin for inhalation delivered by a nebulizer and a compressor in patients with cystic fibrosis (CF) 6 months to 6 years of age.We performed a dose escalation study of serum C(T) measured before and 0.5, 1, 2, and 4 hours after a single dose of inhaled tobramycin, either 180 mg (10 patients) or 300 mg (19 patients). In a separate group of 12 patients, epithelial lining fluid (ELF) C(T) was measured by bronchoalveolar lavage 30 to 45 minutes after a 300-mg dose.A 180-mg dose of inhaled tobramycin produced a mean peak serum C(T) of 0.5 microg/mL (SD 0.4; range,0.2 to 1.4 microg/mL). A 300-mg dose produced a mean peak serum C(T) of 0.6 microg/mL (SD 0.5; range,0.2 to 1.2 microg/mL). These peak values are well below the accepted maximum trough concentration with parenteral dosing (2 microg/mL). The target ELF C(T) was 20 microg/mL, 10-fold greater than the minimal inhibitory concentration for most Pseudomonas aeruginosa isolates from very young patients with CF (2 microg/mL). Mean ELF C(T) was 90 microg/mL (SD 54; range, 16 to 204 microg/mL) and exceeded the target concentration in 11 patients.In patients with CF ages 6 months to 6 years, a single 300-mg dose of inhaled tobramycin appears to produce safe peak serum concentrations and drug concentrations in the bactericidal range in the lower respiratory tract.

Triglyceride (TG) enrichment of high density lipoprotein (HDL), which occurs in hypertriglyceridemic states, significantly enhances the rate at which HDL apolipoprotein (apo)A-I is cleared from the circulation of healthy humans. In the New Zealand White (NZW) rabbit, a species naturally deficient in hepatic lipase (HL), TG enrichment of HDL requires prior lipolytic modification to enhance apoA-I clearance. However, the effect of TG enrichment of HDL on the subsequent clearance of HDL cholesteryl ester (CE) has not previously been examined in vivo. Therefore, we investigated, in the NZW rabbit, the effect of ex vivo TG enrichment of rabbit HDL (by incubation with human very low density lipoprotein) on the clearance of HDL CE and apoA-I radiolabeled with 3H-cholesteryl oleyl ether and with 131I, respectively. In nine experiments, TG enrichment of rabbit HDL resulted in an 87% average increase in HDL TG and a corresponding 31% reduction in HDL CE content. The calculated apoA-I and CE fractional catabolic rates associated with TG-rich versus fasting HDL tracers were not significantly different (apoA-I: 0.119 ± 0.017 vs. 0.107 ± 0.024 pools per h, P = 0.68; CE: 0.147 ± 0.014 vs. 0.114 ± 0.019 pools per h, P = 0.20). In an animal model deficient in HL, TG enrichment of HDL did not alter the rates of HDL apoA-I or selective CE clearance. Further studies are needed to determine whether, in the presence of HL, TG enrichment of HDL alters selective HDL CE clearance. —Rashid, S., K. D. Uffelman, P. H. R. Barrett, P. Vicini, K. Adeli, and G. F. Lewis. Triglyceride enrichment of HDL does not alter HDL-selective cholesteryl ester clearance in rabbits.

The minimal model method is widely used to estimate glucose effectiveness (SG) and insulin sensitivity (SI) from intravenous glucose tolerance test (IVGTT) data. In the standard protocol (sIVGTT, 0.33 g/kg glucose bolus given at time 0), which allows the simultaneous assessment of β-cell function, the precision of the individualized estimates often degrades and particularly so in the presence of reduced sampling schedules. Here, we investigated the use of a population approach, the iterative two-stage (ITS) approach, to analyze 16 sIVGTTs in healthy subjects and to obtain refined estimates of SG and SI in the population and in the individual subjects. The ITS is based on calculation of the population mean and standard deviation of the parameters at each iteration and then use of them as prior information for the individual analyses. Theoretically, the use of a prior in the ITS should improve the precision of the individual estimates. The customary approach (standard two stage, STS), where modeling is performed separately for each individual subject, does not take the population knowledge into account. We used both frequent (FSS, 30 samples) and (quasi-optimally) reduced (RSS, 14 samples) sampling schedules. For the FSS, STS gave estimates (mean ± SD) for SG = 2.66 ± 1.09 × 10−2 · min−1 and SI = 6.46 ± 6.99 10−4 · min−1 · μU−1 · ml, with an average precision of 51 (range 5–176) and 33% (3–91), respectively. RSS radically worsened the precision of both SG and SI. However, RSS and ITS gave SG = 2.59 ± 0.73 and SI = 6.06 ± 7.28, with an average precision of 23 (12–42) and 27% (), respectively. In conclusion, population minimal modeling of sIVGTT data improves the precision of individual estimates of glucose effectiveness and insulin sensitivity, as the theory predicts, and, even with reduced sampling, the improvement is substantial.

In addition to methodological Tutorials,(1) CPT:PSP has recently started to publish software Tutorials.(2,3) Our readership and authors may be wondering what kind of format or product is expected, and the review of submissions we have already received prompted several discussions within the PSP Editorial Team. This editorial reflects on these discussions and summarizes their salient points. It aims at providing some details about the current vision of CPT:PSP for software tutorial articles. In addition, it brings some clarity on the topic of what role commercial software tutorials can have in CPT:PSP and how CPT:PSP tutorials differ from publications which describe the software itself, as those which can be found in other computer science journals. Finally, the discussion includes reproducibility considerations and the general use of commercial and noncommercial software in CPT:PSP publications. We hope our thoughts, and especially a stated requirement to publish user input to the software to aid in reproducibility, will help in guiding our authors and will stimulate healthy debate among our readers about the evolving nature of our science, how it can be facilitated using software and associated databases as a conduit, and what role this journal can play in fostering both the best modeling and simulation practices and the best scientific approaches to computational modeling, to bring the advantages of modeling and simulation to all regular practitioners, and not to just a (self) selected few.

The identifiability problem is addressed for n-compartment linear mammillary and catenary models, for the common case of input and output in the first compartment and prior information about one or more model rate constants. We first define the concept of independent constraints and show that n-compartment linear mammillary or catenary models are uniquely identifiable under n-1 independent constraints. Closed-form algorithms for bounding the constrained parameter space are then developed algebraically, and their validity is confirmed using an independent approach, namely joint estimation of the parameters of all uniquely identifiable submodels of the original multicompartmental model. For the noise-free (deterministic) case, the major effects of additional parameter knowledge are to narrow the bounds of rate constants that remain unidentifiable, as well as to possibly render others identifiable. When noisy data are considered, the means of the bounds of rate constants that remain unidentifiable are also narrowed, but the variances of some of these bound estimates increase. This unexpected result was verified by Monte Carlo simulation of several different models, using both normally and lognormally distributed data assumptions. Extensions and some consequences of this analysis useful for model discrimination and experiment design applications are also noted.