The utility of modeling and simulation in drug development and regulatory review

Allometry Is a Reasonable Choice in Pediatric Drug Development

Pharmacokinetics (PK) plays a key role in bridging drug efficacy and safety from adults to pediatric patients. The principal purpose of projecting dosing in pediatrics is to guide trial design, not to waive the study per se. This research was designed to evaluate whether the allometric scaling (AS) approach is a satisfactory method to design PK studies in pediatric patients aged 2 years and older. We systematically evaluated drugs that had pediatric label information updated from 1998 to 2015. Only intravenous (IV) or oral administration drugs with available PK information in both children and adults from FDA-approved labels were included. The allometric scaling approach was used to extrapolate adult clearance to pediatric clearance. The relative difference between the observed and the allometric scaling approach-predicted clearance was summarized and used to evaluate the predictive power of the allometric scaling approach. A total of 36 drugs eliminated by a metabolic pathway and 10 drugs by the renal pathway after intravenous (IV) or oral administration were included. Regardless of the administration route, elimination pathway, and age group, the allometric scaling approach can predict clearance in pediatric patients within a 2-fold difference; 18 of the included drugs were predicted within a 25% difference, and 31 drugs within a 50% difference. The allometric scaling approach can adequately design PK studies in pediatric subjects 2 years and older.

Extrapolation of Elementary Rate Constants of P-glycoprotein-Mediated Transport from MDCKII-hMDR1-NKI to Caco-2 Cells

The best parameters for incorporation into mechanistic physiologically based pharmacokinetic models for transporters are system-independent kinetic parameters and active (not total) transporter levels. Previously, we determined the elementary rate constants for P-glycoprotein (P-gp)-mediated transport (on- and off-rate constants from membrane to P-gp binding pocket and efflux rate constant into the apical chamber) using the structural mass action kinetic model in confluent MDCKII-hMDR1-NKI cell monolayers. In the present work, we extended the kinetic analysis to Caco-2 cells for the first time and showed that the elementary rate constants are very similar compared with MDCKII-hMDR1-NKI cells, suggesting they primarily depend on the interaction of the compound with P-gp and are therefore mostly independent of the in vitro system used. The level of efflux active (not total) P-gp is also fitted by our model. The estimated level of efflux active P-gp was 5.0 ± 1.4-fold lower in Caco-2 cells than in MDCKII-hMDR1-NKI cells. We also kinetically identified the involvement of a basolateral uptake transporter for both digoxin and loperamide in Caco-2 cells, as found previously in MDCKII-hMDR1-NKI cells, due to their low passive permeability. This demonstrates the value of our P-gp structural model as a diagnostic tool in detecting the importance of other transporters, which cannot be unambiguously done by the Michaelis-Menten approach. The system-independent elementary rate constants for P-gp obtained in vitro are more fundamental parameters than those obtained using Michaelis-Menten steady-state equations. This suggests they will be more robust mechanistic parameters for incorporation into physiologically based pharmacokinetic models for transporters.

IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 2: An introduction to the simulation exercise and overview of results

Orally administered drugs are subject to a number of barriers impacting bioavailability (F_oral), causing challenges during drug and formulation development. Physiologically-based pharmacokinetic (PBPK) modelling can help during drug and formulation development by providing quantitative predictions through a systems approach. The performance of three available PBPK software packages (GI-Sim, Simcyp®, and GastroPlus™) were evaluated by comparing simulated and observed pharmacokinetic (PK) parameters. Since the availability of input parameters was heterogeneous and highly variable, caution is required when interpreting the results of this exercise. Additionally, this prospective simulation exercise may not be representative of prospective modelling in industry, as API information was limited to sparse details. 43 active pharmaceutical ingredients (APIs) from the OrBiTo database were selected for the exercise. Over 4000 simulation output files were generated, representing over 2550 study arm-institution-software combinations and approximately 600 human clinical study arms simulated with overlap. 84% of the simulated study arms represented administration of immediate release formulations, 11% prolonged or delayed release, and 5% intravenous (i.v.). Higher percentages of i.v. predicted area under the curve (AUC) were within two-fold of observed (52.9%) compared to per oral (p.o.) (37.2%), however, F_oral and relative AUC (F_rel) between p.o. formulations and solutions were generally well predicted (64.7% and 75.0%). Predictive performance declined progressing from i.v. to solution and immediate release tablet, indicating the compounding error with each layer of complexity. Overall performance was comparable to previous large-scale evaluations. A general overprediction of AUC was observed with average fold error (AFE) of 1.56 over all simulations. AFE ranged from 0.0361 to 64.0 across the 43 APIs, with 25 showing overpredictions. Discrepancies between software packages were observed for a few APIs, the largest being 606, 171, and 81.7-fold differences in AFE between SimCYP and GI-Sim, however average performance was relatively consistent across the three software platforms.

Translational modeling and simulation approaches for molecularly targeted small molecule anticancer agents from bench to bedside

INTRODUCTION

Recent advances in molecular biology have enabled personalized cancer therapies with molecularly targeted agents (MTAs), which offer a promising future for cancer therapy. Dynamic modeling and simulation (M&S) is a powerful mathematical approach linking drug exposures to pharmacological responses, providing a quantitative assessment of in vivo drug potency. Accordingly, a growing emphasis is being placed upon M&S to quantitatively understand therapeutic exposure-response relationships of MTAs in nonclinical models.

AREAS COVERED

An overview of M&S approaches for MTAs in nonclinical models is presented with discussion about mechanistic extrapolation of antitumor efficacy from bench to bedside. Emphasis is placed upon recent advances in M&S approaches linking drug exposures, biomarker responses (e.g. target modulation) and pharmacological outcomes (e.g. antitumor efficacy).

EXPERT OPINION

For successful personalized cancer therapies with MTAs, it is critical to mechanistically and quantitatively understand their exposure-response relationships in nonclinical models, and to logically and properly apply such knowledge to the clinic. Particularly, M&S approaches to predict pharmacologically active concentrations of MTAs in patients based upon nonclinical data would be highly valuable in guiding the design and execution of clinical trials. Proactive approaches to understand their exposure-response relationships could substantially increase probability of achieving a positive proof-of-concept in the clinic.

IMI - oral biopharmaceutics tools project - evaluation of bottom-up PBPK prediction success part 1: Characterisation of the OrBiTo database of compounds

Predicting oral bioavailability (F_oral) is of importance for estimating systemic exposure of orally administered drugs. Physiologically-based pharmacokinetic (PBPK) modelling and simulation have been applied extensively in biopharmaceutics recently. The Oral Biopharmaceutical Tools (OrBiTo) project (Innovative Medicines Initiative) aims to develop and improve upon biopharmaceutical tools, including PBPK absorption models. A large-scale evaluation of PBPK models may be considered the first step. Here we characterise the OrBiTo active pharmaceutical ingredient (API) database for use in a large-scale simulation study. The OrBiTo database comprised 83 APIs and 1475 study arms. The database displayed a median logP of 3.60 (2.40-4.58), human blood-to-plasma ratio of 0.62 (0.57-0.71), and fraction unbound in plasma of 0.05 (0.01-0.17). The database mainly consisted of basic compounds (48.19%) and Biopharmaceutics Classification System class II compounds (55.81%). Median human intravenous clearance was 16.9L/h (interquartile range: 11.6-43.6L/h; n=23), volume of distribution was 80.8L (54.5-239L; n=23). The majority of oral formulations were immediate release (IR: 87.6%). Human F_oral displayed a median of 0.415 (0.203-0.724; n=22) for IR formulations. The OrBiTo database was found to be largely representative of previously published datasets. 43 of the APIs were found to satisfy the minimum inclusion criteria for the simulation exercise, and many of these have significant gaps of other key parameters, which could potentially impact the interpretability of the simulation outcome. However, the OrBiTo simulation exercise represents a unique opportunity to perform a large-scale evaluation of the PBPK approach to predicting oral biopharmaceutics.

Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine

Personalized medicine strives to deliver the 'right drug at the right dose' by considering inter-person variability, one of the causes for therapeutic failure in specialized populations of patients. Physiologically-based pharmacokinetic (PBPK) modeling is a key tool in the advancement of personalized medicine to evaluate complex clinical scenarios, making use of physiological information as well as physicochemical data to simulate various physiological states to predict the distribution of pharmacokinetic responses. The increased dependency on PBPK models to address regulatory questions is aligned with the ability of PBPK models to minimize ethical and technical difficulties associated with pharmacokinetic and toxicology experiments for special patient populations. Subpopulation modeling can be achieved through an iterative and integrative approach using an adopt, adapt, develop, assess, amend, and deliver methodology. PBPK modeling has two valuable applications in personalized medicine: (1) determining the importance of certain subpopulations within a distribution of pharmacokinetic responses for a given drug formulation and (2) establishing the formulation design space needed to attain a targeted drug plasma concentration profile. This review article focuses on model development for physiological differences associated with sex (male vs. female), age (pediatric vs. young adults vs. elderly), disease state (healthy vs. unhealthy), and temporal variation (influence of biological rhythms), connecting them to drug product formulation development within the quality by design framework. Although PBPK modeling has come a long way, there is still a lengthy road before it can be fully accepted by pharmacologists, clinicians, and the broader industry.

DengueME: A Tool for the Modeling and Simulation of Dengue Spatiotemporal Dynamics

The prevention and control of dengue are great public health challenges for many countries, particularly since 2015, as other arboviruses have been observed to interact significantly with dengue virus. Different approaches and methodologies have been proposed and discussed by the research community. An important tool widely used is modeling and simulation, which help us to understand epidemic dynamics and create scenarios to support planning and decision making processes. With this aim, we proposed and developed DengueME, a collaborative open source platform to simulate dengue disease and its vector's dynamics. It supports compartmental and individual-based models, implemented over a GIS database, that represent Aedes aegypti population dynamics, human demography, human mobility, urban landscape and dengue transmission mediated by human and mosquito encounters. A user-friendly graphical interface was developed to facilitate model configuration and data input, and a library of models was developed to support teaching-learning activities. DengueME was applied in study cases and evaluated by specialists. Other improvements will be made in future work, to enhance its extensibility and usability.

Perspectives in regulatory science: translational and clinical pharmacology

This paper focuses on the role of clinical and translational pharmacology in the drug development and the regulatory process. Contemporary regulatory issues faced by FDA's Office of Clinical Pharmacology (OCP) in fulfilling its mission to advance the science of drug response and translate patient diversity into optimal drug therapy are discussed. Specifically current focus of the following key aspects of the drug development and regulatory science processes are discussed: the OCP vision and mission, two key OCP initiatives (i.e. guidance modernization, labeling and health communications), and translational and clinical pharmacology related regulatory science issues in (i.e. uncertainty, breakthrough therapies, individualization).

In Silico Modeling of Gastrointestinal Drug Absorption: Predictive Performance of Three Physiologically Based Absorption Models

Gastrointestinal (GI) drug absorption is a complex process determined by formulation, physicochemical and biopharmaceutical factors, and GI physiology. Physiologically based in silico absorption models have emerged as a widely used and promising supplement to traditional in vitro assays and preclinical in vivo studies. However, there remains a lack of comparative studies between different models. The aim of this study was to explore the strengths and limitations of the in silico absorption models Simcyp 13.1, GastroPlus 8.0, and GI-Sim 4.1, with respect to their performance in predicting human intestinal drug absorption. This was achieved by adopting an a priori modeling approach and using well-defined input data for 12 drugs associated with incomplete GI absorption and related challenges in predicting the extent of absorption. This approach better mimics the real situation during formulation development where predictive in silico models would be beneficial. Plasma concentration-time profiles for 44 oral drug administrations were calculated by convolution of model-predicted absorption-time profiles and reported pharmacokinetic parameters. Model performance was evaluated by comparing the predicted plasma concentration-time profiles, Cmax, tmax, and exposure (AUC) with observations from clinical studies. The overall prediction accuracies for AUC, given as the absolute average fold error (AAFE) values, were 2.2, 1.6, and 1.3 for Simcyp, GastroPlus, and GI-Sim, respectively. The corresponding AAFE values for Cmax were 2.2, 1.6, and 1.3, respectively, and those for tmax were 1.7, 1.5, and 1.4, respectively. Simcyp was associated with underprediction of AUC and Cmax; the accuracy decreased with decreasing predicted fabs. A tendency for underprediction was also observed for GastroPlus, but there was no correlation with predicted fabs. There were no obvious trends for over- or underprediction for GI-Sim. The models performed similarly in capturing dependencies on dose and particle size. In conclusion, it was shown that all three software packages are useful to guide formulation development. However, as a consequence of the high fraction of inaccurate predictions (prediction error >2-fold) and the clear trend toward decreased accuracy with decreased predicted fabs observed with Simcyp, the results indicate that GI-Sim and GastroPlus perform better than Simcyp in predicting the intestinal absorption of the incompletely absorbed drugs when a higher degree of accuracy is needed. In addition, this study suggests that modeling and simulation research groups should perform systematic model evaluations using their own input data to maximize confidence in model performance and output.

An integrative test strategy for cancer hazard identification

Assessment of genotoxic and carcinogenic potential is considered one of the basic requirements when evaluating possible human health risks associated with exposure to chemicals. Test strategies currently in place focus primarily on identifying genotoxic potential due to the strong association between the accumulation of genetic damage and cancer. Using genotoxicity assays to predict carcinogenic potential has the significant drawback that risks from non-genotoxic carcinogens remain largely undetected unless carcinogenicity studies are performed. Furthermore, test systems already developed to reduce animal use are not easily accepted and implemented by either industries or regulators. This manuscript reviews the test methods for cancer hazard identification that have been adopted by the regulatory authorities, and discusses the most promising alternative methods that have been developed to date. Based on these findings, a generally applicable tiered test strategy is proposed that can be considered capable of detecting both genotoxic as well as non-genotoxic carcinogens and will improve understanding of the underlying mode of action. Finally, strengths and weaknesses of this new integrative test strategy for cancer hazard identification are presented.

Quantitative prediction of human pharmacokinetics and pharmacodynamics of imigliptin, a novel DPP-4 inhibitor, using allometric scaling, IVIVE and PK/PD modeling methods

PURPOSE

To predict the pharmacokinetic/pharmacodynamic (PK/PD) profiles of imigliptin, a novel DPP-4 inhibitor, in first-in-human (FIH) study based on the data from preclinical species.

METHODS

Imigliptin was intravenously and orally administered to rats, dogs, and monkeys to assess their PK/PD properties. DPP-4 activity was the PD biomarker. PK/PD profiles of sitagliptin and alogliptin in rats and humans were obtained and digitized from literatures. PK/PD profiles of all dose levels for each drug in each species were analyzed using modeling approach. Human CL, Vss and PK profiles of imigliptin were then predicted using Allometric Scaling (AS), in vitro in vivo extrapolation (IVIVE), and the steady-state plasma drug concentration - mean residence time (Css-MRT) methods. In vitro EC50 corrected by fu and in vivo EC50 in rats corrected by interspecies difference of sitagliptin and alogliptin were utilized separately to predict imigliptin human EC50. The prediction by integrating all above methods was evaluated by comparing observed and simulated PK/PD profiles in healthy subjects.

RESULTS

Full PK/PD profiles in animal were summarized for imigliptin, sitagliptin and alogliptin. Imigliptin CL, Vss, and Fa were predicted to be 19.1L/h, 247L, and 0.81 in humans, respectively. Predicted imigliptin AUCs, AUECs, and Emax in humans were within 0.8-1.2 times of observed values whereas other predicted PK/PD parameters were within 0.5-1.5 times of observed values.

CONCLUSIONS

By integrating available preclinical and clinical data, FIH PK/PD profiles of imigliptin could be accurately predicted.

Recent Advances in Development and Application of Physiologically-Based Pharmacokinetic (PBPK) Models: a Transition from Academic Curiosity to Regulatory Acceptance

There is a renewed surge of interest in applications of physiologically-based pharmacokinetic (PBPK) models by the pharmaceutical industry and regulatory agencies. Developing PBPK models within a systems pharmacology context allows separation of the parameters pertaining to the animal or human body (the system) from that of the drug and the study design which is essential to develop generic drug-independent models used to extrapolate PK/PD properties in various healthy and patient populations. This has expanded the classical paradigm to a ‘predict-learn-confirm-apply’ concept. Recently, a number of drug labels are informed by simulation results generated using PBPK models. These cases show that either the simulations are used in lieu of conducting clinical studies or have informed the drug label that otherwise would have been silent in some specific situations. It will not be surprising to see applications of these models in implementing precision dosing at the point of care in the near future.

Role of Quantitative Clinical Pharmacology in Pediatric Approval and Labeling

Dose selection is one of the key decisions made during drug development in pediatrics. There are regulatory initiatives that promote the use of model-based drug development in pediatrics. Pharmacometrics or quantitative clinical pharmacology enables development of models that can describe factors affecting pharmacokinetics and/or pharmacodynamics in pediatric patients. This manuscript describes some examples in which pharmacometric analysis was used to support approval and labeling in pediatrics. In particular, the role of pharmacokinetic (PK) comparison of pediatric PK to adults and utilization of dose/exposure-response analysis for dose selection are highlighted. Dose selection for esomeprazole in pediatrics was based on PK matching to adults, whereas for adalimumab, exposure-response, PK, efficacy, and safety data together were useful to recommend doses for pediatric Crohn's disease. For vigabatrin, demonstration of similar dose-response between pediatrics and adults allowed for selection of a pediatric dose. Based on model-based pharmacokinetic simulations and safety data from darunavir pediatric clinical studies with a twice-daily regimen, different once-daily dosing regimens for treatment-naïve human immunodeficiency virus 1-infected pediatric subjects 3 to <12 years of age were evaluated. The role of physiologically based pharmacokinetic modeling (PBPK) in predicting pediatric PK is rapidly evolving. However, regulatory review experiences and an understanding of the state of science indicate that there is a lack of established predictive performance of PBPK in pediatric PK prediction. Moving forward, pharmacometrics will continue to play a key role in pediatric drug development contributing toward decisions pertaining to dose selection, trial designs, and assessing disease similarity to adults to support extrapolation of efficacy.

Top-down, Bottom-up and Middle-out Strategies for Drug Cardiac Safety Assessment via Modeling and Simulations

Cardiac safety is an issue causing early terminations at various stages of drug development. Efforts are put into the elimination of false negatives as well as false positives resulting from the current testing paradigm. In silico approaches offer mathematical system and data description from the ion current, through cardiomyocytes level, up to incorporation of inter-individual variability at the population level. The article aims to review three main modelling and simulation approaches, i.e. "top-down" which refers to models built on the observed data, "bottom-up", which stands for a mechanistic description of human physiology, and "middle-out" which combines both strategies. Modelling and simulation is a well-established tool in the assessment of drug proarrhythmic potency with an impact on research and development as well as on regulatory decisions, and it is certainly here to stay. What is more, the shift to systems biology and physiology-based models makes the cardiac effect more predictable.

Prediction of Drug-Drug Interactions Arising from CYP3A induction Using a Physiologically Based Dynamic Model

Using physiologically based pharmacokinetic modeling, we predicted the magnitude of drug-drug interactions (DDIs) for studies with rifampicin and seven CYP3A4 probe substrates administered i.v. (10 studies) or orally (19 studies). The results showed a tendency to underpredict the DDI magnitude when the victim drug was administered orally. Possible sources of inaccuracy were investigated systematically to determine the most appropriate model refinement. When the maximal fold induction (Ind_max) for rifampicin was increased (from 8 to 16) in both the liver and the gut, or when the Ind_max was increased in the gut but not in liver, there was a decrease in bias and increased precision compared with the base model (Ind_max = 8) [geometric mean fold error (GMFE) 2.12 vs. 1.48 and 1.77, respectively]. Induction parameters (mRNA and activity), determined for rifampicin, carbamazepine, phenytoin, and phenobarbital in hepatocytes from four donors, were then used to evaluate use of the refined rifampicin model for calibration. Calibration of mRNA and activity data for other inducers using the refined rifampicin model led to more accurate DDI predictions compared with the initial model (activity GMFE 1.49 vs. 1.68; mRNA GMFE 1.35 vs. 1.46), suggesting that robust in vivo reference values can be used to overcome interdonor and laboratory-to-laboratory variability. Use of uncalibrated data also performed well (GMFE 1.39 and 1.44 for activity and mRNA). As a result of experimental variability (i.e., in donors and protocols), it is prudent to fully characterize in vitro induction with prototypical inducers to give an understanding of how that particular system extrapolates to the in vivo situation when using an uncalibrated approach.

In Vitro-In Vivo Extrapolation Scaling Factors for Intestinal P-Glycoprotein and Breast Cancer Resistance Protein: Part I: A Cross-Laboratory Comparison of Transporter-Protein Abundances and Relative Expression Factors in Human Intestine and Caco-2 Cells

Over the last 5 years the quantification of transporter-protein absolute abundances has dramatically increased in parallel to the expanded use of in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetics (PBPK)-linked models, for decision-making in pharmaceutical company drug development pipelines and regulatory submissions. Although several research groups have developed laboratory-specific proteomic workflows, it is unclear if the large range of reported variability is founded on true interindividual variability or experimental variability resulting from sample preparation or the proteomic methodology used. To assess the potential for methodological bias on end-point abundance quantification, two independent laboratories, the University of Manchester (UoM) and Bertin Pharma (BPh), employing different proteomic workflows, quantified the absolute abundances of Na/K-ATPase, P-gp, and breast cancer resistance protein (BCRP) in the same set of biologic samples from human intestinal and Caco-2 cell membranes. Across all samples, P-gp abundances were significantly correlated (P = 0.04, Rs = 0.72) with a 2.4-fold higher abundance (P = 0.001) generated at UoM compared with BPh. There was a systematically higher BCRP abundance in Caco-2 cell samples quantified by BPh compared with UoM, but not in human intestinal samples. Consequently, a similar intestinal relative expression factor (REF), derived from distal jejunum and Caco-2 monolayer samples, between laboratories was found for P-gp. However, a 2-fold higher intestinal REF was generated by UoM (2.22) versus BPh (1.11). We demonstrate that differences in absolute protein abundance are evident between laboratories and they probably result from laboratory-specific methodologies relating to peptide choice.

Human Ontogeny of Drug Transporters: Review and Recommendations of the Pediatric Transporter Working Group

The critical importance of membrane-bound transporters in pharmacotherapy is widely recognized, but little is known about drug transporter activity in children. In this white paper, the Pediatric Transporter Working Group presents a systematic review of the ontogeny of clinically relevant membrane transporters (e.g., SLC, ABC superfamilies) in intestine, liver, and kidney. Different developmental patterns for individual transporters emerge, but much remains unknown. Recommendations to increase our understanding of membrane transporters in pediatric pharmacotherapy are presented.

In silico ADME/T modelling for rational drug design

In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.

Prediction of Drug-Drug Interactions with Crizotinib as the CYP3A Substrate Using a Physiologically Based Pharmacokinetic Model

An orally available multiple tyrosine kinase inhibitor, crizotinib (Xalkori), is a CYP3A substrate, moderate time-dependent inhibitor, and weak inducer. The main objectives of the present study were to: 1) develop and refine a physiologically based pharmacokinetic (PBPK) model of crizotinib on the basis of clinical single- and multiple-dose results, 2) verify the crizotinib PBPK model from crizotinib single-dose drug-drug interaction (DDI) results with multiple-dose coadministration of ketoconazole or rifampin, and 3) apply the crizotinib PBPK model to predict crizotinib multiple-dose DDI outcomes. We also focused on gaining insights into the underlying mechanisms mediating crizotinib DDIs using a dynamic PBPK model, the Simcyp population-based simulator. First, PBPK model-predicted crizotinib exposures adequately matched clinically observed results in the single- and multiple-dose studies. Second, the model-predicted crizotinib exposures sufficiently matched clinically observed results in the crizotinib single-dose DDI studies with ketoconazole or rifampin, resulting in the reasonably predicted fold-increases in crizotinib exposures. Finally, the predicted fold-increases in crizotinib exposures in the multiple-dose DDI studies were roughly comparable to those in the single-dose DDI studies, suggesting that the effects of crizotinib CYP3A time-dependent inhibition (net inhibition) on the multiple-dose DDI outcomes would be negligible. Therefore, crizotinib dose-adjustment in the multiple-dose DDI studies could be made on the basis of currently available single-dose results. Overall, we believe that the crizotinib PBPK model developed, refined, and verified in the present study would adequately predict crizotinib oral exposures in other clinical studies, such as DDIs with weak/moderate CYP3A inhibitors/inducers and drug-disease interactions in patients with hepatic or renal impairment.

Logic-Based and Cellular Pharmacodynamic Modeling of Bortezomib Responses in U266 Human Myeloma Cells

Systems models of biological networks show promise for informing drug target selection/qualification, identifying lead compounds and factors regulating disease progression, rationalizing combinatorial regimens, and explaining sources of intersubject variability and adverse drug reactions. However, most models of biological systems are qualitative and are not easily coupled with dynamical models of drug exposure-response relationships. In this proof-of-concept study, logic-based modeling of signal transduction pathways in U266 multiple myeloma (MM) cells is used to guide the development of a simple dynamical model linking bortezomib exposure to cellular outcomes. Bortezomib is a commonly used first-line agent in MM treatment; however, knowledge of the signal transduction pathways regulating bortezomib-mediated cell cytotoxicity is incomplete. A Boolean network model of 66 nodes was constructed that includes major survival and apoptotic pathways and was updated using responses to several chemical probes. Simulated responses to bortezomib were in good agreement with experimental data, and a reduction algorithm was used to identify key signaling proteins. Bortezomib-mediated apoptosis was not associated with suppression of nuclear factor κB (NFκB) protein inhibition in this cell line, which contradicts a major hypothesis of bortezomib pharmacodynamics. A pharmacodynamic model was developed that included three critical proteins (phospho-NFκB, BclxL, and cleaved poly (ADP ribose) polymerase). Model-fitted protein dynamics and cell proliferation profiles agreed with experimental data, and the model-predicted IC50 (3.5 nM) is comparable to the experimental value (1.5 nM). The cell-based pharmacodynamic model successfully links bortezomib exposure to MM cellular proliferation via protein dynamics, and this model may show utility in exploring bortezomib-based combination regimens.

In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond

Drug-drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.

Quantitative global sensitivity analysis of a biologically based dose-response pregnancy model for the thyroid endocrine system

A deterministic biologically based dose-response model for the thyroidal system in a near-term pregnant woman and the fetus was recently developed to evaluate quantitatively thyroid hormone perturbations. The current work focuses on conducting a quantitative global sensitivity analysis on this complex model to identify and characterize the sources and contributions of uncertainties in the predicted model output. The workflow and methodologies suitable for computationally expensive models, such as the Morris screening method and Gaussian Emulation processes, were used for the implementation of the global sensitivity analysis. Sensitivity indices, such as main, total and interaction effects, were computed for a screened set of the total thyroidal system descriptive model input parameters. Furthermore, a narrower sub-set of the most influential parameters affecting the model output of maternal thyroid hormone levels were identified in addition to the characterization of their overall and pair-wise parameter interaction quotients. The characteristic trends of influence in model output for each of these individual model input parameters over their plausible ranges were elucidated using Gaussian Emulation processes. Through global sensitivity analysis we have gained a better understanding of the model behavior and performance beyond the domains of observation by the simultaneous variation in model inputs over their range of plausible uncertainties. The sensitivity analysis helped identify parameters that determine the driving mechanisms of the maternal and fetal iodide kinetics, thyroid function and their interactions, and contributed to an improved understanding of the system modeled. We have thus demonstrated the use and application of global sensitivity analysis for a biologically based dose-response model for sensitive life-stages such as pregnancy that provides richer information on the model and the thyroidal system modeled compared to local sensitivity analysis.

Applications of physiologically based pharmacokinetic modeling for the optimization of anti-infective therapies

INTRODUCTION

The pharmacokinetic properties of anti-infective drugs are a determinant part of treatment success. Pathogen replication is inhibited if adequate drug levels are achieved in target sites, whereas excessive drug concentrations linked to toxicity are to be avoided. Anti-infective distribution can be predicted by integrating in vitro drug properties and mathematical descriptions of human anatomy in physiologically based pharmacokinetic models. This method reduces the need for animal and human studies and is used increasingly in drug development and simulation of clinical scenario such as, for instance, drug-drug interactions, dose optimization, novel formulations and pharmacokinetics in special populations.

AREAS COVERED

We have assessed the relevance of physiologically based pharmacokinetic modeling in the anti-infective research field, giving an overview of mechanisms involved in model design and have suggested strategies for future applications of physiologically based pharmacokinetic models.

EXPERT OPINION

Physiologically based pharmacokinetic modeling provides a powerful tool in anti-infective optimization, and there is now no doubt that both industry and regulatory bodies have recognized the importance of this technology. It should be acknowledged, however, that major challenges remain to be addressed and that information detailing disease group physiology and anti-infective pharmacodynamics is required if a personalized medicine approach is to be achieved.

Application of absorption modeling to predict bioequivalence outcome of two batches of etoricoxib tablets

As part of the overall product development and manufacturing strategy, pharmaceutical companies routinely change formulation and manufacturing site. Depending on the type and level of change and the BCS class of the molecule, dissolution data and/or bioequivalence (BE) may be needed to support the change for immediate release dosage forms. In this report, we demonstrate that for certain weakly basic low-solubility molecules which rapidly dissolve in the stomach, absorption modeling could be used to justify a BE study waiver even when there is failure to show dissolution similarity under some conditions. The development of an absorption model for etoricoxib is described here, which was then used to a priori predict the BE outcome of tablet batches manufactured at two sites. Dissolution studies in 0.01 N HCl media (pH 2.0) had demonstrated similarity of etoricoxib tablets manufactured at two different sites. However, dissolution testing at pH 4.5 and pH 6.8 media failed to show comparability of the tablets manufactured at the two sites. Single simulations and virtual trials conducted using the 0.01 N HCl dissolution showed similarity in AUC and C max for all tablet strengths for batches manufactured at the two manufacturing sites. These predicted results were verified in a definitive bioequivalence study, which showed that both tablet batches were bioequivalent. Since the development of traditional in vitro-in vivo correlations (IVIVC) for immediate release (IR) products is challenging, in cases such as etoricoxib, absorption modeling could be used as an alternative to support waiver of a BE study.

Human pharmacokinetic profiling of the dipeptidyl peptidase-IV inhibitor teneligliptin using physiologically based pharmacokinetic modeling

Teneligliptin is a type 2 diabetes drug that has an inhibitory effect on dipeptidyl peptidase-4. The aim of this study was to establish a physiologically based pharmacokinetic (PBPK) model to elucidate in detail the pharmacokinetics of teneligliptin. A PBPK model of teneligliptin was developed using the population-based Simcyp simulator incorporating the results of in vitro and in vivo studies. Model validation was conducted by comparison of simulated teneligliptin plasma concentrations with those from clinical trials. Using the PBPK model, predicted drug-drug interactions with concomitant medication were examined. The robustness of the PBPK model was demonstrated by the accurate simulation of clinically measured plasma concentrations of teneligliptin after oral administration in different ethnic groups, in subjects belonging to different age groups and in patients with kidney or liver impairment; none of these factors were incorporated during model development. The fraction absorbed and intestinal availability of teneligliptin predicted by the model were 0.62 and 0.99, respectively. The predicted ratios of areas under the time-concentration curves (AUCs) in patients with moderate and severe renal impairment who were concomitantly administered ketoconazole, a potent inhibitor of P450 3A4, were, respectively, 2.1- and 2.2-fold those in healthy adults who were given teneligliptin alone. A robust PBPK model reflecting the pharmacokinetic properties of teneligliptin was constructed. The final optimized PBPK model enabled us to elucidate in detail the factors affecting the pharmacokinetics of teneligliptin and to predict changes in exposure in drug-drug interactions or in specific populations.

Physiologically based pharmacokinetic modeling and simulation in pediatric drug development

Increased regulatory demands for pediatric drug development research have fostered interest in the use of modeling and simulation among industry and academia. Physiologically based pharmacokinetic (PBPK) modeling offers a unique modality to incorporate multiple levels of information to estimate age-specific pharmacokinetics. This tutorial will serve to provide the reader with a basic understanding of the procedural steps to developing a pediatric PBPK model and facilitate a discussion of the advantages and limitations of this modeling technique.

Model-based assessment of dosing strategies in children for monoclonal antibodies exhibiting target-mediated drug disposition

Body weight/body surface area–based and/or tiered fixed dosing strategies are widely utilized for monoclonal antibodies with linear clearance to scale adult clinical doses to children. However, there is limited knowledge on whether or not body weight–based dosing strategies also yield comparable dose-concentration-response relationships in adults and children for monoclonal antibodies that exhibit target-mediated drug disposition. Our findings indicate that it is important to interpret pharmacokinetics information in a pharmacokinetics/pharmacodynamics context as similar systemic drug exposure in adults and children may not be reflective of the corresponding target occupancy. They further indicate that BW-based dosing is superior to fixed dosing for the same target concentration, whereas the opposite holds true for the same target amount in adults and children. Michaelis-Menten approximations yielded similar profiles compared to the full target-mediated drug disposition model for all simulation scenarios and may be used to guide the selection of appropriate dosing regimens in children.

A pharma perspective on the systems medicine and pharmacology of inflammation

Biological systems are complex and comprehend multiple scales of organisation. Hence, holistic approaches are necessary to capture the behaviour of these entities from the molecular and cellular to the whole organism level. This also applies to the understanding and treatment of different diseases. Traditional systems biology has been successful in describing different biological phenomena at the cellular level, but it still lacks of a holistic description of the multi-scale interactions within the body. The importance of the physiological context is of particular interest in inflammation. Regulatory agencies have urged the scientific community to increase the translational power of bio-medical research and it has been recognised that modelling and simulation could be a path to follow. Interestingly, in pharma R&D, modelling and simulation has been employed since a long time ago. Systems pharmacology, and particularly physiologically based pharmacokinetic/pharmacodynamic models, serve as a suitable framework to integrate the available and emerging knowledge at different levels of the drug development process. Systems medicine and pharmacology of inflammation will potentially benefit from this framework in order to better understand inflammatory diseases and to help to transfer the vast knowledge on the molecular and cellular level into a more physiological context. Ultimately, this may lead to reliable predictions of clinical outcomes such as disease progression or treatment efficacy, contributing thereby to a better care of patients.

Application of a physiologically based pharmacokinetic model informed by a top-down approach for the prediction of pharmacokinetics in chronic kidney disease patients

Quantitative prediction of the impact of chronic kidney disease (CKD) on drug disposition has become important for the optimal design of clinical studies in patients. In this study, clinical data of 151 compounds under CKD conditions were extensively surveyed, and alterations in pharmacokinetic parameters were evaluated. In CKD patients, the unbound hepatic intrinsic clearance decreased to a similar extent for drugs eliminated via hepatic metabolism by cytochrome P450, UDP-glucuronosyltransferase, and other mechanisms. Renal clearance showed a similar decrease to glomerular filtration rate, irrespective of the contribution of tubular secretion. The scaling factor (SF) obtained from the interquartile range of the relative change in each parameter was applied to the well-stirred model to predict clearance in patients. Hepatic and renal clearance could be successfully predicted for approximately half and two-thirds, respectively, of the applied compounds, showing the high utility of SFs. SFs were also introduced to a physiologically based pharmacokinetic (PBPK) model, and the plasma concentration profiles of 12 model compounds with different elimination pathways were predicted for CKD patients. The PBPK model combined with SFs provided good predictability for plasma concentration. The developed PBPK model with information on SFs would accelerate translational research in drug development by predicting pharmacokinetics in CKD patients.

Physiologically based pharmacokinetic modeling: from regulatory science to regulatory policy

Assessment of controllable sources of intra- and interpatient variability in drug response is of critical importance in the regulatory evaluation of new drugs.(1) Although determinants of response variability would ideally be understood and accounted for before approval of a new pharmaceutical product, this is rarely the case for all; clinical trials in specific populations that definitively test optimal dosing in patient management strategies are not routinely performed prior to drug approval.

PBPK model describes the effects of comedication and genetic polymorphism on systemic exposure of drugs that undergo multiple clearance pathways

An important goal in drug development is to understand the effects of intrinsic and/or extrinsic factors (IEFs) on drug pharmacokinetics. Although clinical studies investigating a given IEF can accomplish this goal, they may not be feasible for all IEFs or for situations when multiple IEFs exist concurrently. Physiologically based pharmacokinetic (PBPK) models may serve as a complementary tool for forecasting the effects of IEFs. We developed PBPK models for four drugs that are eliminated by both cytochrome P450 (CYP)3A4 and CYP2D6, and evaluated model prediction of the effects of comedications and/or genetic polymorphism on drug exposure. PBPK models predicted 100 and ≥70% of the observed results when the conventional "twofold rule" and the more conservative 25% deviation cut point were applied, respectively. These findings suggest that PBPK models can be used to infer effects of individual or combined IEFs and should be considered to optimize studies that evaluate these factors, specifically drug interactions and genetic polymorphism of drug-metabolizing enzymes.

Basic concepts in population modeling, simulation, and model-based drug development: part 3-introduction to pharmacodynamic modeling methods

Population pharmacodynamic (PD) models describe the time course of drug effects, relating exposure to response, and providing a more robust understanding of drug action than single assessments. PD models can test alternative dose regimens through simulation, allowing for informed assessment of potential dose regimens and study designs. This is the third paper in a three-part series, providing an introduction into methods for developing and evaluating population PD models. Example files are available in the Supplementary Data.

Quantitative prediction of formulation-specific food effects and their population variability from in vitro data with the physiologically-based ADAM model: a case study using the BCS/BDDCS Class II drug nifedipine

Quantitative prediction of food effects (FE) upon drug pharmacokinetics, including population variability, in advance of human trials may help with trial design by optimising the number of subjects and sampling times when a clinical study is warranted or by negating the need for conduct of clinical studies. Classification and rule-based systems such as the BCS and BDDCS and statistical QSARs are widely used to anticipate the nature of FE in early drug development. However, their qualitative rather than quantitative nature makes them less appropriate for assessing the magnitude of FE. Moreover, these approaches are based upon drug properties alone and are not appropriate for estimating potential formulation-specific FE on modified or controlled release products. In contrast, physiologically-based mechanistic models can consider the scope and interplay of a range of physiological changes after food intake and, in combination with appropriate in vitro drug- and formulation-specific data, can make quantitative predictions of formulation-specific FE including the inter-individual variability of such effects. Herein the Advanced Dissolution, Absorption and Metabolism (ADAM) model is applied to the prediction of formulation-specific FE for BCS/BDDCS Class II drug and CYP3A4 substrate nifedipine using as far as possible only in vitro data. Predicted plasma concentration profiles of all three studied formulations under fasted and fed states are within 2-fold of clinically observed profiles. The % prediction error (%PE) in fed-to-fasted ratio of Cmax and AUC were less than 5% for all formulations except for the Cmax of Nifedicron (%PE=-29.6%). This successful case study should help to improve confidence in the use of mechanistic physiologically-based models coupled with in vitro data for the anticipation of FE in advance of in vivo studies. However, it is acknowledged that further studies with drugs/formulations exhibiting a wide range of properties are required to further validate this methodology.