Jogarao V. S. Gobburu

Elucidation of Relationship Between Tumor Size and Survival in Non-Small-Cell Lung Cancer Patients Can Aid Early Decision Making in Clinical Drug Development

Four non‐small‐cell lung cancer (NSCLC) registration trials were utilized to develop models linking survival to risk factors and changes in tumor size during treatment. The purpose was to leverage existing quantitative knowledge to facilitate future development of anti‐NSCLC drugs. Eleven risk factors were screened using a Cox model. A mixed exponential decay and linear growth model was utilized for modeling tumor size. Survival times were described in a parametric model. Eastern Cooperative Oncology Group (ECOG) score and baseline tumor size were consistent prognostic factors of survival. Tumor size was well described by the mixed model. The parametric survival model includes ECOG score, baseline tumor size, and week 8 tumor size change as predictors of survival duration. The change in tumor size at week 8 allows early assessment of the activity of an experimental regimen. The survival model and the tumor model will be beneficial for early screening of candidate drugs, simulating NSCLC trials, and optimizing trial designs.

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.

Impact of Pharmacometric Analyses on New Drug Approval and Labelling Decisions

Pharmacometric analyses have become an increasingly important component of New Drug Application (NDA) and Biological License Application (BLA) submissions to the US FDA to support drug approval, labelling and trial design decisions. Pharmacometrics is defined as a science that quantifies drug, disease and trial information to aid drug development, therapeutic decisions and/or regulatory decisions. In this report, we present the results of a survey evaluating the impact of pharmacometric analyses on regulatory decisions for 198 submissions during the period from 2000 to 2008. Pharmacometric review of NDAs included independent, quantitative analyses by FDA pharmacometricians, even when such analysis was not conducted by the sponsor, as well as evaluation of the sponsor’s report. During 2000–2008, the number of reviews with pharmacometric analyses increased dramatically and the number of reviews with an impact on approval and labelling also increased in a similar fashion. We also present the impact of pharmacometric analyses on selection of paediatric dosing regimens, approval of regimens that had not been directly studied in clinical trials and provision of evidence of effectiveness to support a single pivotal trial. Case studies are presented to better illustrate the role of pharmacometric analyses in regulatory decision making.

Endpoints and Analyses to Discern Disease-Modifying Drug Effects in Early Parkinson’s Disease

Parkinson’s disease is an age-related degenerative disorder of the central nervous system that often impairs the sufferer’s motor skills and speech, as well as other functions. Symptoms can include tremor, stiffness, slowness of movement, and impaired balance. An estimated four million people worldwide suffer from the disease, which usually affects people over the age of 60. Presently, there is no precedent for approving any drug as having a modifying effect (i.e., slowing or delaying) for disease progression of Parkinson’s disease. Clinical trial designs such as delayed start and withdrawal are being proposed to discern symptomatic and protective effects. The current work focused on understanding the features of delayed start design using prior knowledge from published and data submitted to US Food and Drug Administration (US FDA) as part of drug approval or protocol evaluation. Clinical trial simulations were conducted to evaluate the false-positive rate, power under a new statistical analysis methodology, and various scenarios leading to patient discontinuations from clinical trials. The outcome of this work is part of the ongoing discussion between the US FDA and the pharmaceutical industry on the standards required for demonstrating disease-modifying effect using delayed start design.

The Combination of Exposure‐Response and Case‐Control Analyses in Regulatory Decision Making

To reduce the bias introduced by confounding risk factors, a case‐control comparison was incorporated in the exposure‐response (ER) analysis to evaluate the recommended dosing regimen for trastuzumab in a pivotal trial. Results of Kaplan‐Meier survival analysis suggest that patients with metastatic gastric cancer (mGC) in the lowest quartile trough concentrations of trastuzumab in cycle 1 (Cmin 1) had shorter overall survival (OS) than did those in other quartiles. The result of the case‐matched control comparison suggests that adjusting for these risk factors, patients with the lowest quartile of trastuzumab exposure did not benefit from addition of trastuzumab treatment to chemotherapy. The identified subgroup without survival benefit and the ER relationship support the recommendation on conducting clinical trials to identify a treatment regimen with greater exposure and acceptable safety profiles and to prospectively evaluate whether this treatment regimen will result in survival benefit for the identified subgroup.

The product label: how pharmacokinetics and pharmacodynamics reach the prescriber.

The product label, or package insert, is the ‘manual’ for the safe and effective use of a drug. Important pharmacokinetic and pharmacodynamic properties of a drug product should appear in the label under specific sections, as required in the Code of Federal Regulations (CFR), using a format and language recommended by the Food and Drug Administration (FDA) in various guidances to the industry. The relevant regulations and guidance documents impacting on how this information is conveyed to the healthcare professional are discussed, with special emphasis on how the new proposed rule will impact upon how information is to be conveyed. With the availability of new clinical pharmacology information not available at the time of approval, package inserts for older drugs should be updated to reflect the new data and recommend the proper dosage regimen, enabling prescribers to optimise drug therapy and minimise possible adverse events.

Considerations for clinical trial design and data analyses of thorough QT studies using drug-drug interaction.

• J Clin Pharmacol 2010;50:1106-1111 T prolongation of the QT interval associated with polymorphic ventricular tachycardia or torsades de pointes (TdP) is one of the major reasons for market withdrawal of the approved drugs as described in a recent review. The proarrhythmic potential for non-antiarrhythmic drugs is expected to be evaluated in a thorough QT study (TQTS) as described in the International Conference on Harmonisation topic E14 (ICH E14) guideline. It is recommended that the test drug be assessed at supratherapeutic exposures, if not precluded by safety considerations. The supratherapeutic dose should cover the increase in drug and metabolite concentrations in the presence of intrinsic (eg, renal impairment, age, gender) and extrinsic (eg, metabolic inhibition, food effects) factors. Supratherapeutic concentrations are most commonly achieved by administering the test drug at substantial multiples of the highest intended clinical dose. This approach is not always feasible, especially for drugs that have saturable absorption. Alternatively, drug concentrations can be increased by maximally inhibiting metabolism by coadministering a potent metabolic inhibitor (eg, ketoconazole). With this approach, the primary endpoint would be the difference in baseline-adjusted QTc interval between the test drug plus inhibitor and placebo plus inhibitor. The underlying assumption is the inhibitor has no effect on the QT/QTc interval. If this assumption is not valid, then alternative approaches to the analysis would need to be considered. We present a blinded example of a TQTS submitted to the Food and Drug Administration (FDA), for which the sponsor plans to achieve supratherapeutic concentrations by incorporating metabolic inhibition. Major challenges for study design and traditional E14 data analysis are discussed. In addition, we describe a multivariate concentration-QTc model to assess the pharmacokinetic and potential pharmacodynamic interactions between the investigational drug and metabolic inhibitor to determine whether the investigational drug prolongs the QTc interval. To gain confidence in the model we applied, the validity of the structural model is discussed under different simple, yet likely, scenarios. The statistical evaluation of the concentration-QT model is beyond the scope of this article. We believe that this is the first report describing this modeling approach as applied to a TQTS.

Pharmacometrics as a Discipline Is Entering the “Industrialization” Phase: Standards, Automation, Knowledge Sharing, and Training Are Critical for Future Success

9S 0 T development of drug models that incorporate characteristics such as pharmacokinetics, pharmacodynamics, pathophysiology, and genetics has gained importance in drug discovery, development, and innovation in recent years. Integration of drug models with disease progression models has been proposed but has been limited by the lack of contemporary and robust data. In 2004, the US Food and Drug Administration (FDA) Critical Path Initiative identified neuropsychiatric diseases and disease models as priority areas of active research opportunities. The World Health Organization reached similar conclusions that same year. In such a context, precompetitive research, defined as collaborative scientific efforts by entities that ordinarily are commercial competitors, plays a central role. To that end, data-sharing initiatives and the training of professionals with the abilities to tackle such tasks become key needs. In the face of these challenges, industry, regulatory bodies, and academia need to collaboratively address the shared problem of lengthy and expensive medical-product development programs with high attrition rates. This requires not only wellstructured translational sciences and precompetitive research but also a workforce with the necessary training background, for which training and skill building are essential. This article presents the advances being made with respect to developing standards and automation at the FDA, 3 data-sharing initiatives that focus on developing modeling and simulation as a useful tool for drug development, and a discussion about the training of future pharmacometricians.

ASCPT Task Force for advancing pharmacometrics and integration into drug development.

Traditionally, medical and biostatistical experts have played a central role in ensuring validity of pharmaceutical testing. The science of pharmacometrics provides powerful approaches for supporting important drug development and regulatory decisions. Numerous case studies published by academic, industry, and US Food and Drug Administration scientists attest to the significant contribution of pharmacometrics to decision making. The economic and public health benefits of applying this discipline to clinical trials far outweigh the cost associated with its implementation. The purpose of the American Society for Clinical Pharmacology and Therapeutics (ASCPT) Task Force is to build on the momentum and accelerate dissemination of its impact and adoption into drug development. We describe briefly the contributions of pharmacometrics and the specific goals of the Task Force.

Bayesian quantitative disease-drug-trial models for Parkinson's disease to guide early drug development.

The problem we have faced in drug development is in its efficiency. Almost a half of registration trials are reported to fail mainly because pharmaceutical companies employ one-size-fits-all development strategies. Our own experience at the regulatory agency suggests that failure to utilize prior experience or knowledge from previous trials also accounts for trial failure. Prior knowledge refers to both drug-specific and nonspecific information such as placebo effect and the disease course. The information generated across drug development can be systematically compiled to guide future drug development. Quantitative disease–drug–trial models are mathematical representations of the time course of biomarker and clinical outcomes, placebo effects, a drug’s pharmacologic effects, and trial execution characteristics for both the desired and undesired responses. Applying disease–drug–trial model paradigms to design a future trial has been proposed to overcome current problems in drug development. Parkinson’s disease is a progressive neurodegenerative disorder characterized by bradykinesia, rigidity, tremor, and postural instability. A symptomatic effect of drug treatments as well as natural rate of disease progression determines the rate of disease deterioration. Currently, there is no approved drug which claims disease modification. Regulatory agency has been asked to comment on the trial design and statistical analysis methodology. In this work, we aim to show how disease–drug–trial model paradigm can help in drug development and how prior knowledge from previous studies can be incorporated into a current trial using Parkinson’s disease model as an example. We took full Bayesian methodology which can allow one to translate prior information into probability distribution.