Why has model-informed precision dosing not yet become common clinical reality? lessons from the past and a roadmap for the future.

Patient groups prone to polypharmacy and special subpopulations are susceptible to suboptimal treatment. Refined dosing in special populations is imperative to improve therapeutic response and/or lowering the risk of toxicity. Model-informed precision dosing (MIPD) may improve treatment outcomes by achieving the optimal dose for an individual patient. There is, however, relatively little published evidence of large-scale utility and impact of MIPD, where it is often implemented as local collaborative efforts between academia and healthcare. This article highlights some successful applications of bringing MIPD to clinical care and proposes strategies for wider integration in healthcare. Considerations are brought up herein that will need addressing to see MIPD become "widespread clinical practice," among those, wider interdisciplinary collaborations and the necessity for further evidence-based efficacy and cost-benefit analysis of MIPD in healthcare. The implications of MIPD on regulatory policies and pharmaceutical development are also discussed as part of the roadmap.

Identifying clinically relevant sources of variability: The clopidogrel challenge.

High interindividual variability in clinical outcomes following clopidogrel's standard dosing regimen continues to be a challenge even two decades after its approval. CYP2C19 polymorphisms, obesity, older age, diabetes, and drug-drug interactions have been identified as risk factors for adverse events and treatment failure. We conducted a mechanism-based pharmacokinetic/pharmacodynamic analysis, where we integrated knowledge on in vitro enzyme kinetic, physiological, genetic, and demographic information to characterize changes in platelet reactivity from baseline following clopidogrel antiplatelet therapy. When considering the combined impact of these covariates, our analysis results indicate that higher maintenance doses are required for CYP2C19 intermediate metabolizers and poor metabolizers compared to extensive metabolizers and that respective maintenance doses have to be further increased for obese subjects for each of these CYP2C19 phenotypes. In addition, interindividual differences in the fraction absorbed and the CES1 activity were identified as sources of interindividual differences in clopidogrel's active metabolite concentrations and, thus, platelet reactivity.

Quantitative Systems Pharmacology Model to Predict the Effects of Commonly Used Anticoagulants on the Human Coagulation Network.

Warfarin is the anticoagulant of choice for venous thromboembolism (VTE) treatment, although its suppression of the endogenous clot-dissolution complex APC:PS may ultimately lead to longer time-to-clot dissolution profiles, resulting in increased risk of re-thrombosis. This detrimental effect might not occur during VTE treatment using other anticoagulants, such as rivaroxaban or enoxaparin, given their different mechanisms of action within the coagulation network. A quantitative systems pharmacology model was developed describing the coagulation network to monitor clotting factor levels under warfarin, enoxaparin, and rivaroxaban treatment. The model allowed for estimation of all factor rate constants and production rates. Predictions of individual coagulation factor time courses under steady-state warfarin, enoxaparin, and rivaroxaban treatment reflected the suppression of protein C and protein S under warfarin compared to rivaroxaban and enoxaparin. The model may be used as a tool during clinical practice to predict effects of anticoagulants on individual clotting factor time courses and optimize antithrombotic therapy.

Anticoagulants: What is new and what is the standard?

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

Physiologically Based Pharmacokinetics Is Impacting Drug Development and Regulatory Decision Making.

It is no coincidence that the reports of two meetings, one organized by the US Food and Drug Administration (FDA), in March 2014, and the other by the UK Medicines and Healthcare Products Regulatory (MHRA), in collaboration with ABPI (the Association of British Pharmaceutical Industry), in June 2014, have been published in tandem in CPT-PSP.12 Both reports deal with the same topic, namely, the impact of physiologically based pharmacokinetics (PBPK) in clinical drug development and the best practices for such applications. This reflects the transition of PBPK from academic curiosity to industrial norm, manifested by the regulatory agencies encouraging its use and receiving an increasing number of submissions containing PBPK models. The goal of both meetings was to help determine the need and facilitate the development of regulatory guidances on this subject within the conceptual framework of model informed drug development and regulatory decision-making. A further reflection of this intent is the publication by the European Medicines Agency of a Concept Paper on PBPK.3 One is reminded of a similar train of events surrounding the introduction of population PK/PD and nonlinear mixed effects modeling in the early-late 1990s, again with encouragement and receptivity of regulatory agencies leading to FDA guidance on the topic.4 Indeed, the intention of PBPK modeling and simulation is to complement other approaches, such as compartmental modeling, or, in some cases, replace them with a more mechanistic approach. PBPK models represent an important class of models that characterize absorption, distribution, metabolism, excretion (ADME) processes and their underlying biological and physiological drivers. An increased understanding of these drivers and their unique interactions with drug substance and formulation factors provides critical insights into how drugs will behave in healthy volunteers and patients with disease.

Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models.

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

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.

Application of physiologically based pharmacokinetic modeling to predict acetaminophen metabolism and pharmacokinetics in children.

Acetaminophen (APAP) is a widely used analgesic and antipyretic drug that undergoes extensive phase I and II metabolism. To better understand the kinetics of this process and to characterize the dynamic changes in metabolism and pharmacokinetics (PK) between children and adults, we developed a physiologically based PK (PBPK) model for APAP integrating in silico, in vitro, and in vivo PK data into a single model. The model was developed and qualified for adults and subsequently expanded for application in children by accounting for maturational changes from birth. Once developed and qualified, it was able to predict clinical PK data in neonates (0-28 days), infants (29 days to <2 years), children (2 to <12 years), and adolescents (12-17 years) following intravenous and orally administered APAP. This approach represents a general strategy for projecting drug exposure in children, in the absence of pediatric PK information, using previous drug- and system-specific information of adults and children through PBPK modeling.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e80; doi:10.1038/psp.2013.55; advance online publication 16 October 2013.

Systems approaches in risk assessment.

Adverse drug events (ADEs) remain a universal problem in drug development, regulatory review, and clinical practice with a substantial financial burden on the global health-care system. Recent advances in molecular and "omics" technologies, along with online databases and bioinformatics, have enabled a more integrative approach to understanding drug-target (protein) interactions, both desirable and undesirable, within a biological system. This has led to the development of systems approaches to risk assessment in an attempt to complement and improve on contemporary observational and predictive strategies for assessing risk. Although still in an evolutionary phase, systems approaches have the potential to markedly advance our understanding of ADEs and ability to predict them. Systems approaches will also move personalized medicine forward by enabling better identification of individual and subgroup risk factors.

Individualization of drug therapy: history, present state, and opportunities for the future.

Individualization of drug therapy, described as tailoring drug selection and drug dosing to a given patient, has been an objective of physicians and other health-care providers for centuries. An understanding of the pathogenesis of the disease, the mechanism of action of the drug, and exposure-response relationships provides the framework for individualization. There are many approaches to individualization: selecting an antibiotic based on minimum effective concentrations and bacterial sensitivity, population (sparse sample) pharmacokinetics, therapeutic drug monitoring and, more recently, pharmacogenomics. The goal of individualization is to optimize the efficacy of a drug, minimize its toxicity, or both. With the growth of technology and databases, drug-disease-trial models and simulation have become useful for integrating information from many different domains. Physiology-based pharmacokinetic (PBPK) models have provided a mechanistic approach to individualization, and clinical trial designs such as those involving enrichment have also enabled individualization. In the future, "-omics" technologies, vaccines, ex vivo gene therapy, and the so-called "diseases-in-a-dish" will provide additional strategies to achieve individualization.

Quantitative analysis to guide orphan drug development.

The development of orphan drugs for rare diseases has made impressive strides in the past 10 years. There has been a surge in orphan drug designations, but new drug approvals have not kept up. This article presents a three-pronged hierarchical strategy for quantitative analysis of data at the descriptive, mechanistic, and systems levels of the biological system that could represent a standardized and rational approach to orphan drug development. Examples are provided to illustrate the concept.

Clopidogrel: a case for indication-specific pharmacogenetics.

The CYP2C19*2 loss-of-function allele is associated with reduced generation of active metabolites of clopidogrel. However, meta-analyses have supported or discounted the impact of genotype on adverse cardiovascular outcomes during clopidogrel therapy, depending on studies included in the analysis. Here we review these data and conclude that evidence supports a differential effect of genotype on protection from major adverse cardiovascular outcomes following percutaneous coronary intervention (PCI), but not for other clopidogrel indications.

Predicting drug interaction potential with a physiologically based pharmacokinetic model: a case study of telithromycin, a time-dependent CYP3A inhibitor.

Telithromycin is a substrate and an inhibitor of cytochrome P450 3A (CYP3A4), with dose- and time-dependent nonlinear pharmacokinetics (PK). We hypothesized that the time-dependent inhibition (TDI) of CYP3A4 was responsible for the nonlinear PK of telithromycin and then used physiologically based PK (PBPK) modeling and simulation to verify this mechanism. Telithromycin PBPK models integrating in vitro, in silico, and in vivo PK data ruled out the contribution of enzyme/transporter saturation and suggested that TDI is a plausible mechanism for PK nonlinearity. The model successfully predicted the clinical interaction with the CYP3A4 substrate midazolam, as verified by external data not used for the model-building (intravenous (i.v.) and oral (p.o.) midazolam area under the concentration-time curve (AUC) ratio with/without concurrent telithromycin administration: 3.26 and 6.72 predicted vs. 2.20 and 6.11 observed, respectively). Models assuming reversible inhibition failed to predict such strong CYP3A4 inhibition. In the absence of in vitro TDI data, a PBPK model can be used to incorporate TDI mechanisms based on nonlinear PK data to predict clinical drug-drug interactions.

Pharmacological mechanism-based drug safety assessment and prediction.

Advances in cheminformatics, bioinformatics, and pharmacology in the context of biological systems are now at a point that these tools can be applied to mechanism-based drug safety assessment and prediction. The development of such predictive tools at the US Food and Drug Administration (FDA) will complement ongoing efforts in drug safety that are focused on spontaneous adverse event reporting and active surveillance to monitor drug safety. This effort will require the active collaboration of scientists in the pharmaceutical industry, academe, and the National Institutes of Health, as well as those at the FDA, to reach its full potential. Here, we describe the approaches and goals for the mechanism-based drug safety assessment and prediction program.

Transporter-mediated drug-drug interactions.

Transporters are membrane-bound proteins that control the access of endogenous and xenobiotics (drugs) to various sites in the human body. They influence drug pharmacokinetics and pharmacodynamics (both benefit and risk) by affecting a drug's absorption, distribution, metabolism (via control of access to metabolizing enzymes), and excretion (ADME) and by controlling drug concentrations at the site of action. Like metabolizing enzymes, transporters have binding sites that are saturable and can be inhibited or induced.

Applications of physiologically based pharmacokinetic (PBPK) modeling and simulation during regulatory review.

Physiologically based pharmacokinetic (PBPK) modeling and simulation is a tool that can help predict the pharmacokinetics of drugs in humans and evaluate the effects of intrinsic (e.g., organ dysfunction, age, genetics) and extrinsic (e.g., drug-drug interactions) factors, alone or in combinations, on drug exposure. The use of this tool is increasing at all stages of the drug development process. This report reviews recent instances of the use of PBPK in decision-making during regulatory review. The examples are based on Center for Drug Evaluation and Research reviews of several submissions for investigational new drugs (INDs) and new drug applications (NDAs) received between July 2008 and June 2010. The use of PBPK modeling and simulation facilitated the following types of decisions: the need to conduct specific clinical pharmacology studies, specific study designs, and appropriate labeling language. The report also discusses the challenges encountered when PBPK modeling and simulation were used in these cases and recommends approaches to facilitating full utilization of this tool.

Therapeutic protein-drug interactions and implications for drug development.

Many intrinsic and extrinsic factors can affect an individual patient's drug exposure and response. The US Food and Drug Administration (FDA) has published a number of guidances that recommend how and when to evaluate these factors during drug development. The most recent FDA draft guidance on drug interactions provides advice for in vitro and in vivo drug interaction studies, including suggestions for study design, dosing strategies and analysis, and interpretation of data for medical product labels. The draft guidance updated the FDA's recommendations on the evaluation of important cytochrome P450 (CYP) enzyme- and transporter-based drug interactions during drug development.

When to conduct a renal impairment study during drug development: US Food and Drug Administration perspective.

To optimize drug therapy for individuals, it is critical to understand how various intrinsic (e.g., age, gender, race, genetics, organ impairment) and extrinsic factors (e.g., diet, smoking, concomitantly administered drugs) affect drug exposure and response.(1) Up to now, it has been far easier to discover effects on exposure caused by these factors, and the US Food and Drug Administration (FDA) has published several guidance documents with recommendations on how to evaluate these factors during drug development.