Concepts and challenges in quantitative pharmacology and model-based drug development

Mathematical modeling in autoimmune diseases: from theory to clinical application

The research & development (R&D) of novel therapeutic agents for the treatment of autoimmune diseases is challenged by highly complex pathogenesis and multiple etiologies of these conditions. The number of targeted therapies available on the market is limited, whereas the prevalence of autoimmune conditions in the global population continues to rise. Mathematical modeling of biological systems is an essential tool which may be applied in support of decision-making across R&D drug programs to improve the probability of success in the development of novel medicines. Over the past decades, multiple models of autoimmune diseases have been developed. Models differ in the spectra of quantitative data used in their development and mathematical methods, as well as in the level of "mechanistic granularity" chosen to describe the underlying biology. Yet, all models strive towards the same goal: to quantitatively describe various aspects of the immune response. The aim of this review was to conduct a systematic review and analysis of mathematical models of autoimmune diseases focused on the mechanistic description of the immune system, to consolidate existing quantitative knowledge on autoimmune processes, and to outline potential directions of interest for future model-based analyses. Following a systematic literature review, 38 models describing the onset, progression, and/or the effect of treatment in 13 systemic and organ-specific autoimmune conditions were identified, most models developed for inflammatory bowel disease, multiple sclerosis, and lupus (5 models each). ≥70% of the models were developed as nonlinear systems of ordinary differential equations, others - as partial differential equations, integro-differential equations, Boolean networks, or probabilistic models. Despite covering a relatively wide range of diseases, most models described the same components of the immune system, such as T-cell response, cytokine influence, or the involvement of macrophages in autoimmune processes. All models were thoroughly analyzed with an emphasis on assumptions, limitations, and their potential applications in the development of novel medicines.

Clinical and Translational Pharmacology Considerations for Anti-infectives Approved Under the FDA Animal Rule

The US Food and Drug Administration's Animal Rule provides a pathway for approval of drugs and biologics aimed to treat serious or life-threatening conditions wherein traditional clinical trials are either not ethical or feasible. In such a scenario, determination of safety and efficacy are based on integration of data on drug disposition and drug action collected from in vitro models, infected animals, and healthy volunteer human studies. The demonstration of clinical efficacy and safety in humans based on robust, well-controlled animal studies is filled with challenges. This review elaborates on the challenges in the translation of data from in vitro and animal models to human dosing for antimicrobials. In this context, it discusses precedents of drugs approved under the Animal Rule, along with the approaches and guidance undertaken by sponsors.

An intuitive framework for Bayesian posterior simulation methods

Purpose

Bayesian inference has become popular. It offers several pragmatic approaches to account for uncertainty in inference decision-making. Various estimation methods have been introduced to implement Bayesian methods. Although these algorithms are powerful, they are not always easy to grasp for non-statisticians. This paper aims to provide an intuitive framework of four essential Bayesian computational methods for epidemiologists and other health researchers. We do not cover an extensive mathematical discussion of these approaches, but instead offer a non-quantitative description of these algorithms and provide some illuminating examples.

Materials and methods

Bayesian computational methods, namely importance sampling, rejection sampling, Markov chain Monte Carlo, and data augmentation are presented.

Results and conclusions

The substantial amount of research published on Bayesian inference has highlighted its popularity among researchers, while the basic concepts are not always straightforward for interested learners. We show that alternative approaches such as a weighted prior approach, which are intuitively appealing and easy-to-understand, work well in the case of low-dimensional problems and appropriate prior information. Otherwise, MCMC is a trouble-free tool in those cases.

End-to-end application of model-informed drug development for ertugliflozin, a novel sodium-glucose cotransporter 2 inhibitor

Model-informed drug development (MIDD) is critical in all stages of the drug-development process and almost all regulatory submissions for new agents incorporate some form of modeling and simulation. This review describes the MIDD approaches used in the end-to-end development of ertugliflozin, a sodium-glucose cotransporter 2 inhibitor approved for the treatment of adults with type 2 diabetes mellitus. Approaches included (1) quantitative systems pharmacology modeling to predict dose-response relationships, (2) dose-response modeling and model-based meta-analysis for dose selection and efficacy comparisons, (3) population pharmacokinetics (PKs) modeling to characterize PKs and quantify population variability in PK parameters, (4) regression modeling to evaluate ertugliflozin dose-proportionality and the impact of uridine 5'-diphospho-glucuronosyltransferase (UGT) 1A9 genotype on ertugliflozin PKs, and (5) physiologically-based PK modeling to assess the risk of UGT-mediated drug-drug interactions. These end-to-end MIDD approaches for ertugliflozin facilitated decision making, resulted in time/cost savings, and supported registration and labeling.

Application of Pharmacokinetic-Pharmacodynamic Modeling in Drug Delivery: Development and Challenges

With the advancement of technology, drug delivery systems and molecules with more complex architecture are developed. As a result, the drug absorption and disposition processes after administration of these drug delivery systems and engineered molecules become exceedingly complex. As the pharmacokinetic and pharmacodynamic (PK-PD) modeling allows for the separation of the drug-, carrier- and pharmacological system-specific parameters, it has been widely used to improve understanding of the in vivo behavior of these complex delivery systems and help their development. In this review, we summarized the basic PK-PD modeling theory in drug delivery and demonstrated how it had been applied to help the development of new delivery systems and modified large molecules. The linkage between PK and PD was highlighted. In particular, we exemplified the application of PK-PD modeling in the development of extended-release formulations, liposomal drugs, modified proteins, and antibody-drug conjugates. Furthermore, the model-based simulation using primary PD models for direct and indirect PD responses was conducted to explain the assertion of hypothetical minimal effective concentration or threshold in the exposure-response relationship of many drugs and its misconception. The limitations and challenges of the mechanism-based PK-PD model were also discussed.

Pharmacometrics and Machine Learning Partner to Advance Clinical Data Analysis

Clinical pharmacology is a multidisciplinary data sciences field that utilizes mathematical and statistical methods to generate maximal knowledge from data. Pharmacometrics (PMX) is a well-recognized tool to characterize disease progression, pharmacokinetics, and risk factors. Because the amount of data produced keeps growing with increasing pace, the computational effort necessary for PMX models is also increasing. Additionally, computationally efficient methods, such as machine learning (ML) are becoming increasingly important in medicine. However, ML is currently not an integrated part of PMX, for various reasons. The goals of this article are to (i) provide an introduction to ML classification methods, (ii) provide examples for a ML classification analysis to identify covariates based on specific research questions, (iii) examine a clinically relevant example to investigate possible relationships of ML and PMX, and (iv) present a summary of ML and PMX tasks to develop clinical decision support tools.

A tutorial on model informed approaches to cardiovascular safety with focus on cardiac repolarisation

Drugs can affect the cardiovascular (CV) system either as an intended treatment or as an unwanted side effect. In both cases, drug-induced cardiotoxicities such as arrhythmia and unfavourable hemodynamic effects can occur, and be described using mathematical models; such a model informed approach can provide valuable information during drug development and can aid decision-making. However, in order to develop informative models, it is vital to understand CV physiology. The aims of this tutorial are to present (1) key background biological and medical aspects of the CV system, (2) CV electrophysiology, (3) CV safety concepts, (4) practical aspects of development of CV models and (5) regulatory expectations with a focus on using model informed and quantitative approaches to support nonclinical and clinical drug development. In addition, we share several case studies to provide practical information on project strategy (planning, key questions, assumptions setting, and experimental design) and mathematical models development that support decision-making during drug discovery and development.

Pharmacometric Applications and Challenges in the Development of Therapeutic Antibodies in Immuno-Oncology

Purpose of review

Monoclonal antibodies targeting key checkpoints in immune stimulatory pathways have over the last years become the mainstay of cancer immunotherapy. This article provides a brief review of the application and key impact of pharmacometrics and quantitative clinical pharmacology approaches in the development of these novel biologics.

Recent findings

The clinical development and selection of optimal dosing regimens for monoclonal antibodies used in immune-oncology has been facilitated by an extensive application of pharmacometric approaches to characterize the exposure-response relationship for major efficacy and safety endpoints. These analysis techniques were applied for the anti CTLA-4 antibody ipilimumab, as well as the anti PD1/PD-L1 antibodies nivolumab, pembrolizumab, avelumab, atezolizumab and durvalumab. The utilization of quantitative clinical pharmacology, including model-based analyses, did not only support the identification of efficacious doses with acceptable safety limits, but was also able to address complicating challenges such as time- and response-dependent changes in antibody clearance as observed for most compounds.

Summary

A widespread and systematic application of pharmacometric approaches has provided key aspects in elucidating, interpreting and integrating preclinical, biochemical and clinical data in support of the development of safe and efficacious dosing regimens of monoclonal antibodies used in immuno-oncology, thereby facilitating the clinical use of this promising new class of biologics in cancer patients with unmet medical needs.

Model Informed Pediatric Development Applied to Bilastine: Ontogenic PK Model Development, Dose Selection for First Time in Children and PK Study Design

PURPOSE

Bilastine is an H₁ antagonist whose pharmacokinetics (PK) and pharmacodynamics (PD) have been resolved in adults with a therapeutic oral dose of 20 mg/day. Bilastine has favorable characteristics for use in pediatrics but the PK/PD and the optimal dose in children had yet to be clinically explored. The purpose is to: (1) Develop an ontogenic predictive model of bilastine PK linked to the PD in adults by integrating current knowledge; (2) Use the model to design a PK study in children; (3) Confirm the selected dose and the study design through the evaluation of model predictability in the first recruited children; (4) Consider for inclusion the group of younger children (< 6 years).

METHODS

A semi-mechanistic approach was applied to predict bilastine PK in children assuming the same PD as described in adults. The model was used to simulate the time evolution of plasma levels and wheal and flare effects after several doses and design an adaptive PK trial in children that was then confirmed using data from the first recruits by comparing observations with model predictions.

RESULTS

PK/PD simulations supported the selection of 10 mg/day in 2 to <12 year olds. Results from the first interim analysis confirmed the model predictions and design hence trial continuation.

CONCLUSION

The model successfully predicted bilastine PK in pediatrics and optimally assisted the selection of the dose and sampling scheme for the trial in children. The selected dose was considered suitable for younger children and the forthcoming safety study in children aged 2 to <12 years.

Drug Development of Therapeutic Monoclonal Antibodies

Monoclonal antibodies (MAbs) have become a substantial part of many pharmaceutical company portfolios. However, the development process of MAbs for clinical use is quite different than for small-molecule drugs. MAb development programs require careful interdisciplinary evaluations to ensure the pharmacology of both the MAb and the target antigen are well-understood. Selection of appropriate preclinical species must be carefully considered and the potential development of anti-drug antibodies (ADA) during these early studies can limit the value and complicate the performance and possible duration of preclinical studies. In human studies, many of the typical pharmacology studies such as renal or hepatic impairment evaluations may not be needed but the pharmacokinetics and pharmacodynamics of these agents is complex, often necessitating more comprehensive evaluation of clinical data and more complex bioanalytical assays than might be used for small molecules. This paper outlines concerns and strategies for development of MAbs from the early in vitro assessments needed through preclinical and clinical development. This review focuses on how to develop, submit, and comply with regulatory requirements for MAb therapeutics.

Physiologically Based Absorption Modeling to Design Extended-Release Clinical Products for an Ester Prodrug

Absorption modeling has demonstrated its great value in modern drug product development due to its utility in understanding and predicting in vivo performance. In this case, we integrated physiologically based modeling in the development processes to effectively design extended-release (ER) clinical products for an ester prodrug LY545694. By simulating the trial results of immediate-release products, we delineated complex pharmacokinetics due to prodrug conversion and established an absorption model to describe the clinical observations. This model suggested the prodrug has optimal biopharmaceutical properties to warrant developing an ER product. Subsequently, we incorporated release profiles of prototype ER tablets into the absorption model to simulate the in vivo performance of these products observed in an exploratory trial. The models suggested that the absorption of these ER tablets was lower than the IR products because the extended release from the formulations prevented the drug from taking advantage of the optimal absorption window. Using these models, we formed a strategy to optimize the ER product to minimize the impact of the absorption window limitation. Accurate prediction of the performance of these optimized products by modeling was confirmed in a third clinical trial.

Reversible exacerbation of obstructive sleep apnea by α1-adrenergic blockade with tamsulosin: A case report

Obstructive sleep apnea (OSA) is characterized by repeated involuntary closure of the pharyngeal airspace during sleep. Normal activity of the genioglossus (GG) muscle is important in maintaining airway patency, and inhibition of GG activity can contribute to airway closure. Neurons in the hypoglossal motor nucleus (HMN) regulate GG activity. Adrenergic tone is an important regulator of HMN neuronal excitability. In laboratory models α₁-adrenergic antagonists inhibit HMN neurons and GG activity, suggesting that α₁-adrenergic antagonism might adversely affect patients with OSA. To date there has been no report of such a case. Case Summary: The patient was a 67-year old man with a 27-month history of obstructive sleep apnea. Diagnostic polysomnography demonstrated a baseline apnea-hypopnea index (AHI) of 21.3 and a trough oxygen saturation of 84%. Treatment with continuous positive airway pressure (CPAP) was initiated. The AHI in year 1 averaged 1.0 ± 0.1 (mean ± SD) and 0.8 ± 0.1 in year 2. Other medical conditions included hypertension controlled with losartan and benign prostatic hypertrophy not well controlled by finasteride monotherapy. The α₁-adrenergic receptor antagonist tamsulosin 0.4 mg daily was added. Shortly after initiation of tamsulosin, subjective sleep quality deteriorated. Significant surges in obstructive events, apneic episodes, and AHI were also recorded, and nocturnal airway pressure was frequently sustained at the CPAP device maximum of 20 cm H₂O. Tamsulosin was discontinued. CPAP parameters and sleep quality returned to the pre-tamsulosin baselines within 10 days. These findings suggest that α₁-adrenergic blockade with tamsulosin may exacerbate sleep-disordered breathing in susceptible patients.

Revisiting the Middle Molecule Hypothesis of Uremic Toxicity: A Systematic Review of Beta 2 Microglobulin Population Kinetics and Large Scale Modeling of Hemodialysis Trials In Silico

Background

Beta-2 Microglobulin (β2M) is a prototypical “middle molecule” uremic toxin that has been associated with a higher risk of death in hemodialysis patients. A quantitative description of the relative importance of factors determining β2M concentrations among patients with impaired kidney function is currently lacking.

Methods

Herein we undertook a systematic review of existing studies reporting patient level data concerning generation, elimination and distribution of β₂M in order to develop a population model of β₂M kinetics. We used this model and previously determined relationships between predialysis β₂M concentration and survival, to simulate the population distribution of predialysis β₂M and the associated relative risk (RR) of death in patients receiving conventional thrice-weekly hemodialysis with low flux (LF) and high flux (HF) dialyzers, short (SD) and long daily (LD) HF hemodialysis sessions and on-line hemodiafiltration at different levels of residual renal function (RRF).

Results

We identified 9 studies of 106 individuals and 156 evaluations of or more compartmental kinetic parameters of β₂M. These studies used a variety of experimental methods to determine β₂M kinetics ranging from isotopic dilution to profiling of intra/inter dialytic concentration changes. Most of the patients (74/106) were on dialysis with minimal RRF, thus facilitating the estimation of non-renal elimination kinetics of β₂M. In large scale (N = 10000) simulations of individuals drawn from the population of β₂M kinetic parameters, we found that, higher dialytic removal materially affects β₂M exposures only when RRF (renal clearance of β₂M) was below 2 ml/min. In patients initiating conventional HF hemodialysis, total loss of RRF was predicted to be associated with a RR of death of more than 20%. Hemodiafiltration and daily dialysis may decrease the high risk of death of anuric patients by 10% relative to conventional, thrice weekly HF dialysis. Only daily long sessions of hemodialysis consistently reduced mortality risk between 7–19% across the range of β₂M generation rate.

Conclusions

Preservation of RRF should be considered one of the therapeutic goals of hemodialysis practice. Randomized controlled trials of novel dialysis modalities may require large sample sizes to detect an effect on clinical outcomes even if they enroll anuric patients. The developed population model for β₂M may allow personalization of hemodialysis prescription and/or facilitate the design of such studies by identifying patients with higher β₂M generation rate.

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.

Integrating Multiscale Modeling with Drug Effects for Cancer Treatment

In this paper, we review multiscale modeling for cancer treatment with the incorporation of drug effects from an applied system's pharmacology perspective. Both the classical pharmacology and systems biology are inherently quantitative; however, systems biology focuses more on networks and multi factorial controls over biological processes rather than on drugs and targets in isolation, whereas systems pharmacology has a strong focus on studying drugs with regard to the pharmacokinetic (PK) and pharmacodynamic (PD) relations accompanying drug interactions with multiscale physiology as well as the prediction of dosage-exposure responses and economic potentials of drugs. Thus, it requires multiscale methods to address the need for integrating models from the molecular levels to the cellular, tissue, and organism levels. It is a common belief that tumorigenesis and tumor growth can be best understood and tackled by employing and integrating a multifaceted approach that includes in vivo and in vitro experiments, in silico models, multiscale tumor modeling, continuous/discrete modeling, agent-based modeling, and multiscale modeling with PK/PD drug effect inputs. We provide an example application of multiscale modeling employing stochastic hybrid system for a colon cancer cell line HCT-116 with the application of Lapatinib drug. It is observed that the simulation results are similar to those observed from the setup of the wet-lab experiments at the Translational Genomics Research Institute.

Model-based clinical drug development in the past, present and future: a commentary

Clinical drug development remains a mostly empirical, costly enterprise, in which decision-making is often based on qualitative assessment of risk, without properly leveraging all the relevant data collected throughout the development programme. Model-based drug development (MBDD) has been proposed by regulatory agencies, academia and pharmaceutical companies as a paradigm to modernize drug research through the quantification of risk and combination of information from different sources across time. We present here a historical account of the use of MBDD in clinical drug development, the current challenges and further opportunities for its application in the pharmaceutical industry.

Effect of Kidney Function on Drug Kinetics and Dosing in Neonates, Infants, and Children

Neonates, infants, and children differ from adults in many aspects, not just in age, weight, and body composition. Growth, maturation and environmental factors affect drug kinetics, response and dosing in pediatric patients. Almost 80% of drugs have not been studied in children, and dosing of these drugs is derived from adult doses by adjusting for body weight/size. As developmental and maturational changes are complex processes, such simplified methods may result in subtherapeutic effects or adverse events. Kidney function is impaired during the first 2 years of life as a result of normal growth and development. Reduced kidney function during childhood has an impact not only on renal clearance but also on absorption, distribution, metabolism and nonrenal clearance of drugs. 'Omics'-based technologies, such as proteomics and metabolomics, can be leveraged to uncover novel markers for kidney function during normal development, acute kidney injury, and chronic diseases. Pharmacometric modeling and simulation can be applied to simplify the design of pediatric investigations, characterize the effects of kidney function on drug exposure and response, and fine-tune dosing in pediatric patients, especially in those with impaired kidney function. One case study of amikacin dosing in neonates with reduced kidney function is presented. Collaborative efforts between clinicians and scientists in academia, industry, and regulatory agencies are required to evaluate new renal biomarkers, collect and share prospective pharmacokinetic, genetic and clinical data, build integrated pharmacometric models for key drugs, optimize and standardize dosing strategies, develop bedside decision tools, and enhance labels of drugs utilized in neonates, infants, and children.

Modeling and Simulation to Support Phase 2 Dose Selection for RG7652, a Fully Human Monoclonal Antibody Against Proprotein Convertase Subtilisin/Kexin Type 9

RG7652 is a fully humanized monoclonal antibody targeting human PCSK9, a regulator of serum low density lipoprotein cholesterol (LDLc) levels. RG7652 prevents degradation of the hepatic LDLc receptors by blocking PCSK9 binding and thereby resulting in efficient LDLc uptake by hepatocytes. The pharmacokinetics of RG7652 have been evaluated in healthy subjects after single and multiple subcutaneous doses. Pharmacokinetic (PK) and pharmacodynamic (PD) models were developed to explain the antibody PK and LDLc time course data. The PK and PD models based on data from healthy subjects were used to simulate the effects of RG7652 on LDLc levels for a range of potential dose regimens in patients with coronary heart disease. A one-compartment PK model combined with an indirect PD response model was able to adequately describe the PK and LDLc data. Simulations of 400 mg every 4 weeks or 800 mg every 8 weeks regimens show significant LDLc reduction and suggest that dosing RG7652 once every month or once every 2 months is predicted to be optimal for the treatment of hypercholesterolemia. The PK and PD model successfully described the PK and LDLc data from healthy subjects in a Phase 1 study, and the model-based simulations provided useful insights and quantitative understanding for the selection of Phase 2 study doses in patients with coronary heart disease. The approach used in the case study demonstrates the utility of modeling and simulation in designing dose-ranging studies.

Tools for predicting the PK/PD of therapeutic proteins

INTRODUCTION

Assessments of the pharmacokinetic/pharmacodynamic (PK/PD) characteristics are an integral part in the development of novel therapeutic agents. Compared with traditional small molecule drugs, therapeutic proteins possess many distinct PK/PD features that necessitate the application of modified or separate approaches for assessing their PK/PD relationships.

AREAS COVERED

In this review, the authors discuss tools that are utilized to describe and predict the PK/PD features of therapeutic proteins and that are valuable additions in the armamentarium of drug development approaches to facilitate and accelerate their successful preclinical and clinical development.

EXPERT OPINION

A variety of state-of-the-art PK/PD tools is currently being applied and has been adjusted to support the development of proteins as therapeutics, including allometric scaling approaches, target-mediated disposition models, first-in-man dose calculations, physiologically based PK models and empirical and semi-mechanistic PK/PD modeling. With the advent of the next generation of biologics including bioengineered antibody constructs being developed, these tools will need to be further refined and adapted to ensure their applicability and successful facilitation of the drug development process for these novel scaffolds.

Interactive Pharmacometric Applications Using R and the Shiny Package

Interactive applications, developed using Shiny for the R programming language, have the potential to revolutionize the sharing and communication of pharmacometric model simulations. Shiny allows customization of the application's user-interface to provide an elegant environment for displaying user-input controls and simulation output-where the latter simultaneously updates with changing input. The flexible nature of the R language makes simulations of population variability possible thus promoting the combination of Shiny with R in model visualization.

At the bench: the key role of PK-PD modelling in enabling the early discovery of biologic therapies

Pharmacokinetic-pharmacodynamic (PK-PD) modelling is already used extensively in pre-clinical and clinical drug development to characterize drug candidates quantitatively, aid go/no-go decisions and to inform future trial design and optimal dosing regimens. Less well known, although arguably as powerful, is its application at the earliest stages of drug development, at target selection and lead selection, where these same techniques can be used to predict and so bring forward drug candidates with the necessary characteristics or, for unachievable requirements, allow the abandonment of the programme for the minimum spend of time and cost. We consider three examples that illustrate the power of the application of modelling at this early stage. We start with the simple case of determining the optimal characteristics for a monoclonal antibody against a soluble ligand with its application to the investment decision for the development of best-in-class compounds. This is extended to the more complex situation of the target protein having an endogenous, inhibitory binding protein. We then illustrate how using physiologically-based pharmacokinetic modelling enables the appropriate engineering and testing of biological therapeutics for optimal PK-PD characteristics. These examples illustrate how a minimal investment in modelling achieves orders of magnitude better returns in choosing the correct targets, mechanism of action and candidate characteristics to progress to clinical trials, streamlining drug development and delivering better medicines to patients.

Dashboard systems: implementing pharmacometrics from bench to bedside

In recent years, there has been increasing interest in the development of medical decision-support tools, including dashboard systems. Dashboard systems are software packages that integrate information and calculations about therapeutics from multiple components into a single interface for use in the clinical environment. Given the high cost of medical care, and the increasing need to demonstrate positive clinical outcomes for reimbursement, dashboard systems may become an important tool for improving patient outcome, improving clinical efficiency and containing healthcare costs. Similarly the costs associated with drug development are also rising. The use of model-based drug development (MBDD) has been proposed as a tool to streamline this process, facilitating the selection of appropriate doses and making informed go/no-go decisions. However, complete implementation of MBDD has not always been successful owing to a variety of factors, including the resources required to provide timely modeling and simulation updates. The application of dashboard systems in drug development reduces the resource requirement and may expedite updating models as new data are collected, allowing modeling results to be available in a timely fashion. In this paper, we present some background information on dashboard systems and propose the use of these systems both in the clinic and during drug development.

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.

Approaches to in vitro tissue regeneration with application for human disease modeling and drug development

Reliable in vitro human disease models that capture the complexity of in vivo tissue behaviors are crucial to gain mechanistic insights into human disease and enable the development of treatments that are effective across broad patient populations. The integration of stem cell technologies, tissue engineering, emerging biomaterials strategies and microfabrication processes, as well as computational and systems biology approaches, is enabling new tools to generate reliable in vitro systems to study the molecular basis of human disease and facilitate drug development. In this review, we discuss these recently developed tools and emphasize opportunities and challenges involved in combining these technologies toward regenerative science.

Pharmacokinetics and pharmacokinetic-pharmacodynamic correlations of therapeutic peptides

Peptides, defined as polymers of less than 50 amino acids with a molecular weight of less than 10 kDa, represent a fast-growing class of new therapeutics which has unique pharmacokinetic characteristics compared to large proteins or small molecule drugs. Unmodified peptides usually undergo extensive proteolytic cleavage, resulting in short plasma half-lives. As a result of their low permeability and susceptibility to catabolic degradation, therapeutic peptides usually have very limited oral bioavailability and are administered either by the intravenous, subcutaneous, or intramuscular route, although other routes such as nasal delivery are utilized as well. Distribution processes are mainly driven by a combination of diffusion and to a lesser degree convective extravasation dependent on the size of the peptide, with volumes of distribution frequently not larger than the volume of the extracellular body fluid. Owing to the ubiquitous availability of proteases and peptidases throughout the body, proteolytic degradation is not limited to classic elimination organs. Since peptides are generally freely filtered by the kidneys, glomerular filtration and subsequent renal metabolism by proteolysis contribute to the elimination of many therapeutic peptides. Although small peptides have usually limited immunogenicity, formation of anti-drug antibodies with subsequent hypersensitivity reactions has been described for some peptide therapeutics. Numerous strategies have been applied to improve the pharmacokinetic properties of therapeutic peptides, especially to overcome their metabolic instability, low permeability, and limited tissue residence time. Applied techniques include amino acid substitutions, modification of the peptide terminus, inclusion of disulfide bonds, and conjugation with polymers or macromolecules such as antibody fragments or albumin. Application of model-based pharmacokinetic-pharmacodynamic correlations has been widely used for therapeutic peptides in support of drug development and dosage regimen design, especially because their targets are often well-described endogenous regulatory pathways and processes.

Modeling of human tumor xenografts and dose rationale in oncology

Xenograft models are commonly used in oncology drug development. Although there are discussions about their ability to generate meaningful data for the translation from animal to humans, it appears that better data quality and better design of the preclinical experiments, together with appropriate data analysis approaches could make these data more informative for clinical development. An approach based on mathematical modeling is necessary to derive experiment-independent parameters which can be linked with clinically relevant endpoints. Moreover, the inclusion of biomarkers as predictors of efficacy is a key step towards a more general mechanism-based strategy.

Applications of pharmacometrics in the clinical development and pharmacotherapy of anti-infectives

With the increased emergence of anti-infective resistance in recent years, much focus has recently been drawn to the development of new anti-infectives and the optimization of treatment regimens and combination therapies for established antimicrobials. In this context, the field of pharmacometrics using quantitative numerical modeling and simulation techniques has in recent years emerged as an invaluable tool in the pharmaceutical industry, academia and regulatory agencies to facilitate the integration of preclinical and clinical development data and to provide a scientifically based framework for rational dosage regimen design and treatment optimization. This review highlights the usefulness of pharmacometric analyses in anti-infective drug development and applied pharmacotherapy with select examples.

Pharmacometrics: opportunity for reducing disease burden in the developing world: the case of Africa

Pharmacometricians are virtually nonexistent in Africa and the developing world. The unrelenting burden of infectious diseases, which are often treated using medicines with narrow effectiveness and safety dose ranges, and the growing prevalence and recognition of non-communicable diseases represent significant threats for the patients, although affording an opportunity for advancing science. This article outlines the case for pharmacometricians to redirect their expertise to focus on the disease burden affecting the developing world.

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework—a dynamic knowledge repository—wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline. © 2013 Wiley Periodicals, Inc.

White paper: landscape on technical and conceptual requirements and competence framework in drug/disease modeling and simulation

Pharmaceutical sciences experts and regulators acknowledge that pharmaceutical development as well as drug usage requires more than scientific advancements to cope with current attrition rates/therapeutic failures. Drug disease modeling and simulation (DDM&S) creates a paradigm to enable an integrated and higher-level understanding of drugs, (diseased)systems, and their interactions (systems pharmacology) through mathematical/statistical models (pharmacometrics)¹—hence facilitating decision making during drug development and therapeutic usage of medicines. To identify gaps and challenges in DDM&S, an inventory of skills and competencies currently available in academia, industry, and clinical practice was obtained through survey. The survey outcomes revealed benefits, weaknesses, and hurdles for the implementation of DDM&S. In addition, the survey indicated that no consensus exists about the knowledge, skills, and attributes required to perform DDM&S activities effectively. Hence, a landscape of technical and conceptual requirements for DDM&S was identified and serves as a basis for developing a framework of competencies to guide future education and training in DDM&S.

Dose selection using a semi-mechanistic integrated glucose-insulin-glucagon model: designing phase 2 trials for a novel oral glucokinase activator

Selecting dosing regimens for phase 2 studies for a novel glucokinase activator LY2599506 is challenging due to the difficulty in modeling and assessing hypoglycemia risk. A semi-mechanistic integrated glucose-insulin-glucagon (GIG) model was developed in NONMEM based on pharmacokinetic, glucose, insulin, glucagon, and meal data obtained from a multiple ascending dose study in patients with Type 2 diabetes mellitus treated with LY2599506 for up to 26 days. The series of differential equations from the NONMEM model was translated into an R script to prospectively predict 24-h glucose profiles following LY2599506 treatment for 3 months for a variety of doses and dosing regimens. The reduction in hemoglobin A1c (HbA1c) at the end of the 3-month treatment was estimated using a transit compartment model based on the simulated fasting glucose values. Two randomized phase 2 studies, one with fixed dosing and the other employing conditional dose titration were conducted. The simulation suggested that (1) Comparable HbA1c lowering with lower hypoglycemia risk occurs with titration compared to fixed-dosing; and (2) A dose range of 50-400 mg BID provides either greater efficacy or lower hypoglycemia incidence or both than glyburide. The predictions were in reasonable agreement with the observed clinical data. The model predicted HbA1c reduction and hypoglycemia risk provided the basis for the decision to focus on the dose-titration trial and for the selection of doses for the demonstration of superiority of LY2599506 to glyburide. The integrated GIG model represented a valuable tool for the evaluation of hypoglycemia incidence.

Population pharmacokinetics and pharmacodynamics of bivalirudin in young healthy Chinese volunteers

AIM

To investigate the population pharmacokinetics (PK) and pharmacodynamics (PD) of bivalirudin, a synthetic bivalent direct thrombin inhibitor, in young healthy Chinese subjects.

METHODS

Thirty-six young healthy volunteers were randomly assigned into 4 groups received bivalirudin 0.5 mg/kg, 0.75 mg/kg, and 1.05 mg/kg intravenous bolus, 0.75 mg/kg intravenous bolus followed by 1.75 mg/kg intravenous infusion per hour for 4 h. Blood samples were collected to measure bivalirudin plasma concentration and activated clotting time (ACT). Population PK-PD analysis was performed using the nonlinear mixed-effects model software NONMEM. The final models were validated with bootstrap and prediction-corrected visual predictive check (pcVPC) approaches.

RESULTS

The final PK model was a two-compartment model without covariates. The typical PK population values of clearance (CL), apparent distribution volume of the central-compartment (V(1)), inter-compartmental clearance (Q) and apparent distribution volume of the peripheral compartment (V(2)) were 0.323 L·h(-1)·kg(-1), 0.086 L/kg, 0.0957 L·h(-1)·kg(-1), and 0.0554 L/kg, respectively. The inter-individual variabilities of these parameters were 14.8%, 24.2%, fixed to 0% and 15.6%, respectively. The final PK-PD model was a sigmoid E(max) model without the Hill coefficient. In this model, a covariate, red blood cell count (RBC(*)), had a significant effect on the EC(50) value. The typical PD population values of maximum effect (E(max)), EC(50), baseline ACT value (E(0)) and the coefficient of RBC(*) on EC(50) were 318 s, 2.44 mg/L, 134 s and 1.70, respectively. The inter-individual variabilities of E(max), EC(50), and E(0) were 6.80%, 46.4%, and 4.10%, respectively.

CONCLUSION

Population PK-PD models of bivalirudin in healthy young Chinese subjects have been developed, which may provide a reference for future use of bivalirudin in China.

Integrating experimentation and quantitative modeling to enhance discovery of Beta amyloid lowering therapeutics for Alzheimer's disease

Drug discovery can benefit from a proactive-knowledge-attainment philosophy which strategically integrates experimentation and pharmacokinetic/pharmacodynamic (PK/PD) modeling. Our programs for Alzheimer’s disease (AD) illustrate such an approach. Compounds that inhibit the generation of brain beta amyloid (Aβ), especially Aβ42, are being pursued as potential disease-modifying therapeutics. Complexities in the PK/Aβ relationship for these compounds have been observed and the data require an advanced approach for analysis. We established a semimechanistic PK/PD model that can describe the PK/Aβ data by accounting for Aβ generation and clearance. The modeling characterizes the in vivo PD (i.e., Aβ lowering) properties of compounds and generates insights about the salient biological systems. The learning from the modeling enables us to establish a framework for predicting in vivo Aβ lowering from in vitro parameters.

Mathematical modeling of tumor growth and tumor growth inhibition in oncology drug development

INTRODUCTION

Approaches aiming to model the time course of tumor growth and tumor growth inhibition following a therapeutic intervention have recently been proposed for supporting decision making in oncology drug development. When considered in a comprehensive model-based approach, tumor growth can be included in the cascade of quantitative and causally related markers that lead to the prediction of survival, the final clinical response.

AREAS COVERED

The authors examine articles dealing with the modeling of tumor growth and tumor growth inhibition in both preclinical and clinical settings. In addition, the authors review models describing how pharmacological markers can be used to predict tumor growth and models describing how tumor growth can be linked to survival endpoints.

EXPERT OPINION

Approaches and success stories of application of model-based drug development centered on tumor growth modeling are growing. It is also apparent that these approaches can answer practical questions on drug development more effectively than that in the past. For modeling purposes, some improvements are still needed related to study design and data quality. Further efforts are needed to encourage the mind shift from a simple description of data to the prediction of untested conditions that modeling approaches allow.

Design, conduct, analysis, and interpretation of clinical studies in patients with impaired kidney function

Chronic kidney disease has been shown to alter the pharmacokinetics of drugs that are eliminated not only via the renal pathway but also by metabolism or nonrenal transport. Guidance documents from regulatory agencies on the pharmacokinetics of drugs in patients with impaired kidney function provide a framework for facilitating study design, conduct, data analysis, and the generation of dosing recommendations. Design considerations include establishment of appropriate enrollment criteria, selection of appropriate matched control group(s), and staging of impaired kidney function by estimated glomerular filtration rate or creatinine clearance. When studies in hemodialysis patients are conducted, optimizing the timing of characterization of the pharmacokinetics profile based on the schedule of hemodialysis sessions will allow for a robust assessment in these patients. In addition to traditional noncompartmental approaches, the use of pharmacometric approaches can integrate data from multiple clinical studies and provide a quantitative rationale for dose selection in patients with impaired kidney function. This article addresses the challenges and opportunities associated with the design, conduct, analysis, and interpretation of clinical studies to allow for their future facilitation and for the establishment of safe and efficacious dosing in patients with impaired kidney function.

Exploratory translational modeling approach in drug development to predict human brain pharmacokinetics and pharmacologically relevant clinical doses

The central nervous system (CNS) pharmacokinetics (PK) of drugs that have pharmacological targets in the brain are not often understood during drug development, and this gap in knowledge is a limitation in providing a quantitative framework for translating nonclinical pharmacologic data to the clinical patient population. A focus of translational sciences is to improve the efficiency of clinical trial design via a more judicious selection of clinical doses on the basis of nonclinical data. We hypothesize that this can be achieved for CNS-acting drugs based on knowledge of CNS PK and brain target engagement obtained in nonclinical studies. Translating CNS PK models from rat to human can allow for the prediction of human brain PK and the human dose-brain exposure relationship, which can provide insight on the clinical dose(s) having potential brain activity and target engagement. In this study, we explored the potential utility of this translational approach using rat brain microdialysis and PK modeling techniques to predict human brain extracellular fluid PK of atomoxetine and duloxetine. The results show that this translational approach merits consideration as a means to support the clinical development of CNS-mediated drug candidates by enhancing the ability to predict pharmacologically relevant doses in humans in the absence of or in association with other biomarker approaches.