Impact of population pharmacokinetic-pharmacodynamic analyses on the drug development process: experience at Parke-Davis

A multicompartment population PK model to predict tenofovir and emtricitabine mucosal tissue concentrations for HIV prevention

A priori use of mathematical modeling and simulation to predict outcomes from incomplete adherence or reduced frequency dosing strategies may mitigate the risk of clinical trial failure with HIV pre-exposure prophylaxis regimens. We developed a semi-physiologic population pharmacokinetic model for two antiretrovirals and their active intracellular metabolites in three mucosal tissues using pharmacokinetic data from a phase I, dose-ranging study. Healthy female volunteers were given a single oral dose of tenofovir disoproxil fumarate (150, 300, or 600 mg) or emtricitabine (100, 200, or 400 mg). Simultaneous co-modeling of all data was performed on a Linux cluster. A 16 compartment, bolus input, linear kinetic model best described the data, containing 986 observations in 23 individuals across three matrices and four analytes. Combined with a defined efficacious concentration target in mucosal tissues, this model can be used to optimize the dose and dosing frequency through Monte-Carlo simulations.

Pharmacometrics: The Already-Present Future of Precision Pharmacology

The use of mathematical modeling to represent, analyze, make predictions or providing information on data obtained in drug research and development has made pharmacometrics an area of great prominence and importance. The main purpose of pharmacometrics is to provide information relevant to the search for efficacy and safety improvements in pharmacotherapy. Regulatory agencies have adopted pharmacometrics analysis to justify their regulatory decisions, making those decisions more efficient. Demand for specialists trained in the field is therefore growing. In this review, we describe the meaning, history, and development of pharmacometrics, analyzing the challenges faced in the training of professionals. Examples of applications in current use, perspectives for the future, and the importance of pharmacometrics for the development and growth of precision pharmacology are also presented.

Antiplatelet Effect of a Pulaimab [Anti-GPIIb/IIIa F(ab)2 Injection] Evaluated by a Population Pharmacokinetic-pharmacodynamic Model

BACKGROUND

Cardiovascular disease has one of the highest mortality rates among all the diseases. Platelets play an important role in the pathogenesis of cardiovascular diseases. Platelet membrane glycoprotein GPIIb/IIIa antagonists are the most effective antiplatelet drugs, and pulaimab is one of these. The study aims to promote individual medication of pulaimab [anti-GPIIb/IIIa F(ab)2 injection] by discovering the pharmacological relationship among the dose, concentration, and effects. The goal of this study is to establish a population pharmacokineticpharmacodynamic model to evaluate the antiplatelet effect of intravenous pulaimab injection.

METHODS

Data were collected from 59 healthy subjects who participated in a Phase-I clinical trial. Plasma concentration was used as the pharmacokinetic index, and platelet aggregation inhibition rate was used as the pharmacodynamic index. The basic pharmacokinetics model was a two-compartment model, whereas the basic pharmacodynamics model was a sigmoid-EMAX model with a direct effect. The covariable model was established by a stepwise method. The final model was verified by a goodness-of-fit method, and predictive performance was assessed by a Bootstrap (BS) method.

RESULTS

In the final model, typical population values of the parameters were as follows: central distribution Volume (V1), 183 L; peripheral distribution Volume (V2), 349 L; Central Clearance (CL), 31 L/h; peripheral clearance(Q), 204 L/h; effect compartment concentration reaching half of the maximum effect (EC50), 0.252 mg/L; maximum effect value (EMAX), 54.0%; and shape factor (γ), 0.42. In the covariable model, thrombin time had significant effects on CL and EMAX. Verification by the goodness-of-fit and BS methods showed that the final model was stable and reliable.

CONCLUSION

A model was successfully established to evaluate the antiplatelet effect of intravenous pulaimab injection that could provide support for the clinical therapeutic regimen.

Role of modelling and simulation: a European regulatory perspective

Modelling and simulation (M&S) of clinical data, e.g. pharmacokinetic, pharmacodynamic and clinical endpoints, is a useful approach for more efficient interpretation of collected data and for extrapolation of knowledge to the entire target population. This type of documentation is included in the majority of marketing authorization applications for new medicinal products. This article summarizes the current status of regulatory review with respect to the role of M&S in Europe from the perspective of the Swedish Medical Products Agency. At present, regulatory bodies in Europe encourage the application of the M&S approach during drug development. However, there is a lack of consensus and transparent guidance documents. The main regulatory usage is in the evaluation of dose choices in sub-populations and as support for the dosing regimen in general. The regulatory review of conestat alfa illustrates how the dose recommendation was revised during the approval procedure based on M&S information. A survey of marketing authorization applications for new medicinal products approved in 2010 revealed that the use of the information gained from M&S documentation varies with respect to both regulatory review and the applicants' presentation of the data in the submitted dossier. Increased utilization and broadened application of M&S is anticipated in pharmaceutical development, where one area of focus is medicines for paediatric patients. Accordingly, the regulatory agencies will need to increase their capability to assess and utilize this type of information, and an interactive process among regulatory agencies is warranted to provide more unified regulatory assessment and guidance. Moreover, applicants are encouraged to expand on the usage of exposure-response models to map the systemic exposure range that yields safe and efficacious treatment and to improve the presentation of the gained knowledge in summary documents of the marketing authorization applications.

Pharmacometrics 2020

This special issue of the Journal of Clinical Pharmacology is dedicated to pharmacometrics, covering topics related to methodological research, application to decisions, standardization, PhRMA survey, and growth strategy. Innovative methodological and technological advances in analyzing disease, drug, and trial data have equipped pharmacometricians with the know-how to influence high-level decisions, which in turn creates more pharmacometric opportunities. Pharmacometrics is revolutionizing drug development and regulatory decision making. To sustain the success and growth of this field, we need to up the ante. Strategic goals for pharmacometric groups in industry, regulatory agencies, and academia are proposed in this report. These goals should be of significance to all stakeholders who have a vested interest in drug development and therapeutics. The future of pharmacometrics depends on how well we all can deliver on the strategic goals.

Model-based drug development: strengths, weaknesses, opportunities, and threats for broad application of pharmacometrics in drug development

Systematic implementation of model-based drug development (MBDD) to drug discovery and development has the potential to significantly increase the rate of medical breakthroughs and make available new and better treatments to patients. An analysis of the strengths, weaknesses, opportunities, and threats (ie, SWOT) was conducted through focus group discussions that included 24 members representing 8 pharmaceutical companies to systematically assess the challenges to implementing MBDD into the drug development decision-making process. The application of the SWOT analysis to the successful implementation of MBDD yielded 19 strengths, 27 weaknesses, 34 opportunities, and 22 threats, which support the following conclusions. The shift from empirical drug development to MBDD requires a question-based mentality; early, proactive planning; dynamic access to multisource data; quantitative knowledge integration; multidisciplinary collaboration; effective communication and leadership skills; and innovative, impactful application of pharmacometrics focused on enhancing quantitative decision making. The ultimate goal of MBDD is to streamline discovery and development of innovative medicines to benefit patients.

Quantitative clinical pharmacology is transforming drug regulation

Prior to 1970s, development and regulation of new drugs was devoid of a fully quantitative, pathophysiological conceptual foundation. Malcolm Rowland pioneered, in collaboration with colleagues and friends, our modern understanding of drug clearance concepts, and equipped drug development and regulatory scientists with key investigative tools such as physiologically-based pharmacokinetic (PBPK) modeling, standardized approaches to characterizing drug metabolism, and microdosing. From the 1970s to the present, Malcolm Rowland has contributed to key advances in pharmacokinetics that have had transformational impacts on drug regulatory science. These advances include concepts that have led to the fundamental understanding that mechanistically derived, quantitative variations in drug concentrations, rather than assigned dosage alone, drive pharmacodynamic effects (PKPD)-including disease biomarkers and clinical outcomes. This body of knowledge has transformed drug development and regulatory science theory and practice from naïve empiricism to a mechanism/model-based, quantitative scientific discipline. As a result, it is now possible to incorporate pre-clinical in vitro data on drug physico-chemical properties, metabolizing enzymes, transporters and permeability properties into PBPK-based simulations of expected PK distributions and drug-drug interactions in human populations. The most comprehensive application of PK-PD is in the modeling and simulation of clinical trials in the context of model-based drug development and regulation, imbedded in the "learn-confirm paradigm". Regulatory agencies have embraced these advances and incorporated them into regulatory requirements, approval acceleration pathways and regulatory decisions. These developments are reviewed here, with emphasis on key contributions of Malcolm Rowland that facilitated this transformation.

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

Model-based drug development (MBDD) has been recognized as a concept to improve the efficiency of drug development. The acceptance of MBDD from regulatory agencies, industry, and academia has been growing, yet today's drug development practice is still distinctly distant from MBDD. This manuscript is aimed at clarifying the concept of MBDD and proposing practical approaches for implementing MBDD in the pharmaceutical industry. The following concepts are defined and distinguished: PK-PD modeling, exposure-response modeling, pharmacometrics, quantitative pharmacology, and MBDD. MBDD is viewed as a paradigm and a mindset in which models constitute the instruments and aims of drug development efforts. MBDD covers the whole spectrum of the drug development process instead of being limited to a certain type of modeling technique or application area. The implementation of MBDD requires pharmaceutical companies to foster innovation and make changes at three levels: (1) to establish mindsets that are willing to get acquainted with MBDD, (2) to align processes that are adaptive to the requirements of MBDD, and (3) to create a closely collaborating organization in which all members play a role in MBDD. Pharmaceutical companies that are able to embrace the changes MBDD poses will likely be able to improve their success rate in drug development, and the beneficiaries will ultimately be the patients in need.

Leveraging prior quantitative knowledge to guide drug development decisions and regulatory science recommendations: impact of FDA pharmacometrics during 2004-2006

The End-of-Phase 2A meetings are proposed to identify opportunities to make innovative medical products available sooner and to increase the quality of drug applications through early meetings between sponsors and the FDA. This article summarizes the overall experience across 11 pilot End-of-Phase 2A meetings since 2004. Four case studies are presented in more detail to demonstrate the various issues and methods encountered at these meetings. Overall, industry and FDA scientists ranked these meetings to be "very helpful" (average score of 4 on a scale of 1 to 5). In almost all the instances the sponsors changed their drug development plans subsequent to these extensive quantitative analyses-based meetings. A draft Guidance is being developed to be issued in 2008, and we hope this initiative will be resourced by then.

Integrated pharmacokinetics and pharmacodynamics in drug development

Drug development is a complex, lengthy and expensive process. Pharmaceutical companies and regulatory authorities have recognised that the drug development process needs optimisation for efficiency in view of the return on investments. Pharmacokinetics and pharmacodynamics are the two main principles determining the relationship between dose and response. This article provides an update on integrated approaches towards drug development by linking pharmacokinetics, pharmacodynamics and disease aspects into mathematical models. Gradually, a transition is taking place from a rather empirical approach towards a modelling- and simulation-based approach to drug development. The main learning phases should be phases 0, I and II, whereas phase III studies should merely have a confirmatory purpose. In model-based drug development, mechanism-based mathematical models, which are iteratively refined along the path of development, incorporate the accumulating knowledge of the investigational drug, the disease and their mutual interference in different subsets of the target population. These models facilitate the design of the next study and improve the probability of achieving the projected efficacy and safety endpoints. In this article, several theoretical and practical aspects of an integrated approach towards drug development are discussed, together with some case studies from different therapeutic areas illustrating the application of pharmacokinetic/pharmacodynamic disease models at different stages of drug development.

Pharmacometrics and the transition to model-based development

As the transition to model-based drug development continues, pharmacometric analysis will have an increasingly important role across the entire life cycle of drug discovery, development, regulatory approval, and commercialization. For this reason, pharmacometrics can--and should--have an integrating function in the transformation to model-based development. This essay describes an approach for formalizing the pharmacometrics process using the disciplines encompassed by enterprise engineering.

Impact of Pharmacometric Reviews on New Drug Approval and Labeling Decisions—a Survey of 31 New Drug Applications Submitted Between 2005 and 2006

Exploratory analyses of data pertaining to pharmacokinetic, pharmacodynamic, and disease progression are often referred to as the pharmacometrics (PM) analyses. The objective of the current report is to assess the role of PM, at the Food and Drug Administration (FDA), in drug approval and labeling decisions. We surveyed the impact of PM analyses on New Drug Applications (NDAs) reviewed over 15 months in 2005-2006. The survey focused on both the approval and labeling decisions through four perspectives: clinical pharmacology primary reviewer, their team leader, the clinical team member, and the PM reviewer. A total of 31 NDAs included a PM review component. Review of NDAs involved independent quantitative evaluation by FDA pharmacometricians. PM analyses were ranked as important in regulatory decision making in over 85% of the 31 NDAs. Case studies are presented to demonstrate the applications of PM analysis.

Impact of pharmacometrics on drug approval and labeling decisions: a survey of 42 new drug applications

The value of quantitative thinking in drug development and regulatory review is increasingly being appreciated. Modeling and simulation of data pertaining to pharmacokinetic, pharmacodynamic, and disease progression is often referred to as the pharmacometrics analyses. The objective of the current report is to assess the role of pharmacometrics at the US Food and Drug Administration (FDA) in making drug approval and labeling decisions. The New Drug Applications (NDAs) submitted between 2000 and 2004 to the Cardio-renal, Oncology, and Neuropharmacology drug products divisions were surveyed. For those NDA reviews that included a pharmacometrics consultation, the clinical pharmacology scientists ranked the impact on the regulatory decision(s). Of about a total of 244 NDAs, 42 included a pharmacometrics component. Review of NDAs involved independent, quantitative evaluation by FDA pharmacometricians, even when such analysis was not conducted by the sponsor. Pharmacometric analyses were pivotal in regulatory decision making in more than half of the 42 NDAs. Of the 14 reviews that were pivotal to approval related decisions, 5 identified the need for additional trials, whereas 6 reduced the burden of conducting additional trials. Collaboration among the FDA clinical pharmacology, medical, and statistical reviewers and effective communication with the sponsors was critical for the impact to occur. The survey and the case studies emphasize the need for early interaction between the FDA and sponsors to plan the development more efficiently by appreciating the regulatory expectations better.

Population pharmacokinetics III: design, analysis, and application of population pharmacokinetic Studies

OBJECTIVE

To present a framework within which population pharmacokinetic (PPK) studies should be designed and analyzed and discuss the application of developed PPK models.

METHODS

Information on PPK was retrieved from a MEDLINE search (1979-December 2003) of the literature and a bibliographic evaluation of review articles and books. This information is used in conjunction with experience to explain the design and analysis of PPK studies. Also, examples are included to demonstrate the usefulness of PPK.

SYNTHESIS

A great deal of thought must be given to the design and analysis of PPK studies (ie, development of PPK models). Models are of 2 primary types--descriptive and predictive--and the process applied to these models is necessarily different. An approach that ensures model applicability is presented.

CONCLUSIONS

PPK models have great utility, and the applications are many. They are very different from single-subject pharmacokinetic models and therefore require different approaches to model estimation.

Pharmacokinetic-pharmacodynamic guided trial design in oncology

The application of pharmacokinetic (PK) and pharmacodynamic (PD) modeling in drug development has emerged during the past decades and it is has been suggested that the investigation of PK-PD relationships during drug development may facilitate and optimize the design of subsequent clinical development. Especially in oncology, well designed PK-PD modeling could be extremely useful as anticancer agents usually have a very narrow therapeutic index. This paper describes the application of the current insights in the use of PK-PD modeling to the design of clinical trials in oncology. The application of PK-PD modeling in each separate stage of (pre)clinical drug development of anticancer agents is discussed. The implementation of this approach is illustrated with the clinical development of docetaxel.

Pharmacokinetic/pharmacodynamic studies in drug product development

In the quest of ways for rationalizing and accelerating drug product development, integrated pharmacokinetic/pharmacodynamic (PK/PD) concepts provide a highly promising tool. PK/PD modeling concepts can be applied in all stages of preclinical and clinical drug development, and their benefits are multifold. At the preclinical stage, potential applications might comprise the evaluation of in vivo potency and intrinsic activity, the identification of bio-/surrogate markers, as well as dosage form and regimen selection and optimization. At the clinical stage, analytical PK/PD applications include characterization of the dose-concentration-effect/toxicity relationship, evaluation of food, age and gender effects, drug/drug and drug/disease interactions, tolerance development, and inter- and intraindividual variability in response. Predictive PK/PD applications can also involve extrapolation from preclinical data, simulation of drug responses, as well as clinical trial forecasting. Rigorous implementation of the PK/PD concepts in drug product development provides a rationale, scientifically based framework for efficient decision making regarding the selection of potential drug candidates, for maximum information gain from the performed experiments and studies, and for conducting fewer, more focused clinical trials with improved efficiency and cost effectiveness. Thus, PK/PD concepts are believed to play a pivotal role in streamlining the drug development process of the future.

The role of population pharmacokinetics in drug development in light of the Food and Drug Administration's 'Guidance for Industry: population pharmacokinetics'

Population pharmacokinetics (PPK) has evolved from a discipline primarily applied to therapeutic drug monitoring to one that plays a significant role in clinical pharmacology in general and drug development in particular. In February 1999 the US Food and Drug Administration issued a 'Guidance for Industry: Population Pharmacokinetics' that sets out the mechanisms and philosophy of PPK and outlines its role in drug development. The application of PPK to the drug development process plays an important role in the efficient development of safe and effective drugs. PPK knowledge is essential for mapping the response surface, explaining subgroup differences, developing and evaluating competing dose administration strategies, and as an aid in designing future studies. The mapping of the response surface is done to maximise the benefit-risk ratio, so that the impact of the input profile and dose magnitude on beneficial and harmful pharmacological effects can be understood and applied to individual patients. PPK combined with simulation methods provides a tool for estimating the expected range of concentrations from competing dose administration strategies. Once extracted, this knowledge can be applied to labelling or used to assess various future study designs. PPK should be implemented across all phases of drug development. For preclinical studies, PPK can be applied to allometric scaling and toxicokinetic analyses, and is useful for determining 'first time in man' doses and explaining toxicological results. Phase I studies provide initial understanding of the structural model and the effect of possible covariates, and may later be used to evaluate PPK differences between patients and healthy individuals. Phase II studies provide the greatest opportunity to map the response surface. With these PPK models it is possible to gain an improved understanding of the role of the dose on the response surface and of the range of expected responses. In phase III and IV studies, PPK is implemented to further refine the PPK model and to explain unexpected responses. Planning for the implementation of PPK across all phases of drug development is necessary, as well as planning for individual PPK studies. Planning should include: defining important questions, identifying covariates and drug-drug interactions that need to be investigated, and identifying the applications and intended use of the model(s). The plan for each project must have a strategy for data management, data collection, data quality assurance, staff training for data collection, data analysis and model validation.