Recent Advances in Development and Application of Physiologically-Based Pharmacokinetic (PBPK) Models: a Transition from Academic Curiosity to Regulatory Acceptance
14 Apr 2016-Current Pharmacology Reports (Springer International Publishing)-Vol. 2, Iss: 3, pp 161-169
TL;DR: A number of drug labels are informed by simulation results generated using PBPK models, showing 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.
Abstract: 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.
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Federal University of Paraíba1, University of North Carolina at Chapel Hill2, University of Bonn3, Merck & Co.4, University of Copenhagen5, University of New Mexico6, University of Gothenburg7, University of Strasbourg8, Moscow State University9, University of Florida10, Duke University11, North Carolina State University12, University of California, Los Angeles13, University of Toronto14, Novartis15, University of British Columbia16
TL;DR: This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed inQSAR to a wide range of research areas outside of traditional QSar boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics.
Abstract: Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure–activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
383 citations
University of Manchester1, Cincinnati Children's Hospital Medical Center2, University of North Carolina at Chapel Hill3, Geneva College4, Pfizer5, University of Florida6, University of Washington7, Leiden University8, Erasmus University Rotterdam9, Children's Mercy Hospital10, Children's Hospital Los Angeles11, Children's Hospital of Philadelphia12, MedImmune13
TL;DR: 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.
Abstract: 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.
143 citations
Cites background from "Recent Advances in Development and ..."
...There has been an increase in the use of modeling to inform drug labeling over the last decade, mainly for interpolation of the magnitude of metabolic DDIs, where 61% of applications of PBPK M&S were dedicated to the prediction of DDIs.29,41,42 Nonetheless, when dedicated studies are considered as the norm to provide dosing guidance it may lead to imprecise (and offlabel) dosing in healthcare for special populations not explicitly addressed in the label, as indicated recently by Jadhav et al.,29 keeping in mind that it is practically impossible to conduct specific studies for all the permutation of possible combinations of comorbidities....
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...Here MIPD can provide not only informative and quantitative answers regarding dose optimization, but could also be used to select the appropriate drugs to avoid DDIs....
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...Mechanistic models, such as PBPK, allow for extrapolation of exposure from patients represented in phase II–III efficacy-safety studies to special populations through perturbation of model structure or parameters, as seen in cases such as pregnancy, obesity, and for DDIs.10,23,25,59,60 Although currently PBPK M&S may be considered the best suited alternative for prediction/extrapolation of initial dosing in a drug-population combination that has not been previously studied, the ability to incorporate all the patient information seamlessly from the patient records at the point of care is not in place....
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...There has been an increase in the use of modeling to inform drug labeling over the last decade, mainly for interpolation of the magnitude of metabolic DDIs, where 61% of applications of PBPK M&S were dedicated to the prediction of DDIs.(29,41,42) Nonetheless, when dedicated studies are considered as the norm to provide dosing guidance it may lead to imprecise (and offlabel) dosing in healthcare for special populations not explicitly addressed in the label, as indicated recently by Jadhav et al....
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TL;DR: Current in vitro, in vivo, and in silico platforms for modelling healthy and pathological cardiac tissues and their advantages and disadvantages for drug screening and discovery applications are described and a roadmap for employing these non-animal platforms in assessing drug cardiotoxicity and safety is suggested.
138 citations
TL;DR: This article aims to enhance a previously published first-in-human physiologically based pharmacokinetic model-building strategy by reviewing many relevant scientific publications to identify new findings and highlight gaps that need to be addressed.
Abstract: Physiologically based pharmacokinetic modelling is well established in the pharmaceutical industry and is accepted by regulatory agencies for the prediction of drug–drug interactions. However, physiologically based pharmacokinetic modelling is valuable to address a much wider range of pharmaceutical applications, and new regulatory impact is expected as its full power is leveraged. As one example, physiologically based pharmacokinetic modelling is already routinely used during drug discovery for in-vitro to in-vivo translation and pharmacokinetic modelling in preclinical species, and this leads to the application of verified models for first-in-human pharmacokinetic predictions. A consistent cross-industry strategy in this application area would increase confidence in the approach and facilitate further learning. With this in mind, this article aims to enhance a previously published first-in-human physiologically based pharmacokinetic model-building strategy. Based on the experience of scientists from multiple companies participating in the GastroPlus™ User Group Steering Committee, new Absorption, Distribution, Metabolism and Excretion knowledge is integrated and decision trees proposed for each essential component of a first-in-human prediction. We have reviewed many relevant scientific publications to identify new findings and highlight gaps that need to be addressed. Finally, four industry case studies for more challenging compounds illustrate and highlight key components of the strategy.
84 citations
TL;DR: This is the first data set to describe in vitro properties for 45 legacy and development anti-malarial drugs and will be a valuable tool for malaria researchers aiming to develop PBPK models for the prediction of human PK properties and/or drug–drug interactions.
Abstract: Modelling and simulation are being increasingly utilized to support the discovery and development of new anti-malarial drugs. These approaches require reliable in vitro data for physicochemical properties, permeability, binding, intrinsic clearance and cytochrome P450 inhibition. This work was conducted to generate an in vitro data toolbox using standardized methods for a set of 45 anti-malarial drugs and to assess changes in physicochemical properties in relation to changing target product and candidate profiles. Ionization constants were determined by potentiometric titration and partition coefficients were measured using a shake-flask method. Solubility was assessed in biorelevant media and permeability coefficients and efflux ratios were determined using Caco-2 cell monolayers. Binding to plasma and media proteins was measured using either ultracentrifugation or rapid equilibrium dialysis. Metabolic stability and cytochrome P450 inhibition were assessed using human liver microsomes. Sample analysis was conducted by LC–MS/MS. Both solubility and fraction unbound decreased, and permeability and unbound intrinsic clearance increased, with increasing Log D7.4. In general, development compounds were somewhat more lipophilic than legacy drugs. For many compounds, permeability and protein binding were challenging to assess and both required the use of experimental conditions that minimized the impact of non-specific binding. Intrinsic clearance in human liver microsomes was varied across the data set and several compounds exhibited no measurable substrate loss under the conditions used. Inhibition of cytochrome P450 enzymes was minimal for most compounds. This is the first data set to describe in vitro properties for 45 legacy and development anti-malarial drugs. The studies identified several practical methodological issues common to many of the more lipophilic compounds and highlighted areas which require more work to customize experimental conditions for compounds being designed to meet the new target product profiles. The dataset will be a valuable tool for malaria researchers aiming to develop PBPK models for the prediction of human PK properties and/or drug–drug interactions. Furthermore, generation of this comprehensive data set within a single laboratory allows direct comparison of properties across a large dataset and evaluation of changing property trends that have occurred over time with changing target product and candidate profiles.
79 citations
References
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TL;DR: Improvement in parameter prediction was largely due to the incorporation of distribution processes related to drug ionisation, an issue that is not addressed in earlier equations.
733 citations
TL;DR: Specific advances and contemporary challenges with respect to predicting the processes of drug clearance, distribution, and absorption are reviewed, together with the ability to anticipate the quantitative extent of PK-based drug-drug interactions and the impact of age, genetics, disease, and formulation.
Abstract: The application of physiologically-based pharmacokinetic (PBPK) modeling is coming of age in drug development and regulation, reflecting significant advances over the past 10 years in the predictability of key pharmacokinetic (PK) parameters from human in vitro data and in the availability of dedicated software platforms and associated databases. Specific advances and contemporary challenges with respect to predicting the processes of drug clearance, distribution, and absorption are reviewed, together with the ability to anticipate the quantitative extent of PK-based drug-drug interactions and the impact of age, genetics, disease, and formulation. The value of this capability in selecting and designing appropriate clinical studies, its implications for resource-sparing techniques, and a more holistic view of the application of PK across the preclinical/clinical divide are considered. Finally, some attention is given to the positioning of PBPK within the drug development and approval paradigm and its future application in truly personalized medicine.
573 citations
"Recent Advances in Development and ..." refers background in this paper
...The majority of early applications of PBPK models deal with issues related to anaesthesia and risk assessment of environmental chemicals due to their capability to predict the systemic exposure of chemicals in various parts of the body [30]....
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...It is suggested [30] that the origins of PBPKmodels go back to the work of Teorell in 1937 [36]....
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TL;DR: An effort to build virtual populations using extensive demographic, physiological, genomic and in vitro biochemical data to simulate and predict drug disposition from routinely collected in vitro data is outlined.
Abstract: The perceived failure of new drug development has been blamed on deficiencies in in vivo studies of drug efficacy and safety. Prior simulation of the potential exposure of different individuals to a given dose might help to improve the design of such studies. This should also help researchers to focus on the characteristics of individuals who present with extreme reactions to therapy. An effort to build virtual populations using extensive demographic, physiological, genomic and in vitro biochemical data to simulate and predict drug disposition from routinely collected in vitro data is outlined.
494 citations
"Recent Advances in Development and ..." refers methods in this paper
...extrapolation (IVIVE) techniques [28]; & Methods to predict tissue partition coefficients using physicochemical properties and protein binding data [24, 25]....
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TL;DR: Recent instances of the use of PBPK in decision‐making during regulatory review are reviewed, based on Center for Drug Evaluation and Research reviews of several submissions for investigational new drugs and new drug applications received between July 2008 and June 2010.
Abstract: 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.
437 citations
"Recent Advances in Development and ..." refers background in this paper
...This in turn helps with designing and optimising clinical studies and selecting the optimal dosing regimens [42]....
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...This expands the classical paradigm to ‘predict-learn-confirm-apply’ [41, 42] that stretches the spectrum of M&S from early drug discovery to beyond phase III clinical studies....
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TL;DR: A cross pharmaceutical industry position on “how PBPK modeling can be applied in industry” is provided focusing on the strategies for application of P BPK at different stages, an associated perspective on the confidence and challenges, as well as guidance on interacting with regulatory agencies and internal best practices.
Abstract: The application of physiologically based pharmacokinetic (PBPK) modeling has developed rapidly within the pharmaceutical industry and is becoming an integral part of drug discovery and development. In this study, we provide a cross pharmaceutical industry position on "how PBPK modeling can be applied in industry" focusing on the strategies for application of PBPK at different stages, an associated perspective on the confidence and challenges, as well as guidance on interacting with regulatory agencies and internal best practices.
361 citations
"Recent Advances in Development and ..." refers background in this paper
...The reports from both workshops as well as an industry perspective are now published [18, 35, 40]....
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