Model-based evaluation of the efficacy and safety of nivolumab once every 4 weeks across multiple tumor types.

BACKGROUND: Nivolumab 480 mg every 4 weeks (Q4W) is approved in the European Union, United States, and several other markets across multiple tumor types. Its approval was supported by quantitative efficacy/safety analyses bridging to 3 mg/kg every 2 weeks (Q2W). PATIENTS AND METHODS: The benefit-risk profile of nivolumab 480 mg Q4W relative to 3 mg/kg Q2W was evaluated using population pharmacokinetic modeling and exposure-response (E-R) analyses for safety and efficacy. Pharmacokinetic exposures were predicted for 3203 patients with melanoma, non-small-cell lung cancer (NSCLC), renal cell carcinoma (RCC), squamous cell carcinoma of the head and neck, urothelial carcinoma, or classical Hodgkin lymphoma. Quantitative models analyzed E-R to predict 480-mg Q4W safety across all indications and efficacy for melanoma, NSCLC, and RCC. Intratumoral receptor occupancy (RO) was predicted for parameters representing different tumor types. RESULTS: Time-averaged concentrations for 480 mg Q4W versus 3 mg/kg Q2W were higher during the first 28 days (26.8%) and similar at steady state (5.2%). The maximum concentration (Cmax) after the first dose was higher (110.4%), and the trough concentration at day 28 was lower (-22.1%) with 480 mg Q4W versus 3 mg/kg Q2W. The Cmax achieved with 480 mg Q4W was lower than the previously established safe dose of 10 mg/kg Q2W. The probability of adverse events for key safety end points was similar for 480 mg Q4W and 3 mg/kg Q2W. The predicted overall survival and objective response rates with 480 mg Q4W were comparable to 3 mg/kg Q2W. The predicted high intratumoral RO provided additional evidence to support 480 mg Q4W across tumor types. CONCLUSIONS: The benefit-risk profile for nivolumab 480 mg Q4W was predicted to be similar to that of 3 mg/kg Q2W across tumor types while providing a convenient and flexible option for patients and their caregivers.

Pharmacometrics at FDA: evolution and impact on decisions.

Drug development and regulatory decisions are driven by information that is compiled primarily from clinical trials and other supportive experiments, but also through clinical experience in the post-market period. The wisdom of these decisions determines the efficiency of drug development, the decision to approve the drug, and the resultant drug product quality including guidance on how to use the product known as the label. Although the decisions are usually simple in nature (e.g., trial design and project progression at the company, product and labeling approval at the Food and Drug Administration (FDA)), the information informing the decision is complex and diverse.

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.

Communicating to Influence Drug Development and Regulatory Decisions: A Tutorial.

Pharmacometricians require three skills to be influential: technical, business (e.g., drug development), and soft skills (e.g., communication). Effective communication is required to translate technical and often complicated quantitative findings to interdisciplinary team members in order to influence drug development or regulatory decisions. In this tutorial, we highlight important aspects related to communicating pharmacometric analysis to influence decisions.

Drugs Against Rare Diseases: Are The Regulatory Standards Higher?

The US Food and Drug Administration (FDA) recently issued a draft Guidance for Industry for Rare Diseases: Common Issues in Drug Development (referred to as "Rare Diseases Guidance"). In our opinion, the FDA should consider: (a) explicitly acknowledging the standards are higher for rare diseases for the reasons presented in this article; and (b) illustrating innovative development pathways that may be acceptable for rare diseases, including case studies.

Characterizing Class-Specific Exposure-Viral Load Suppression Response of HIV Antiretrovirals Using A Model-Based Meta-Analysis.

We applied model-based meta-analysis of viral suppression as a function of drug exposure and in vitro potency for short-term monotherapy in human immunodeficiency virus type 1 (HIV-1)-infected treatment-naïve patients to set pharmacokinetic targets for development of nonnucleoside reverse transcriptase inhibitors (NNRTIs) and integrase strand transfer inhibitors (InSTIs). We developed class-specific models relating viral load kinetics from monotherapy studies to potency normalized steady-state trough plasma concentrations. These models were integrated with a literature assessment of doses which demonstrated to have long-term efficacy in combination therapy, in order to set steady-state trough concentration targets of 6.17- and 2.15-fold above potency for NNRTIs and InSTIs, respectively. Both the models developed and the pharmacokinetic targets derived can be used to guide compound selection during preclinical development and to predict the dose-response of new antiretrovirals to inform early clinical trial design.

Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities.

Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety.

Role of dosage regimen in controlling indirect pharmacodynamic responses.

The role of drug delivery in controlling indirect pharmacodynamic responses was assessed via computer simulations and literature review. Simulations of responses related to basic indirect response mechanisms were performed for various drug input rates which allowed the importance of drug delivery rate on the overall pharmacodynamic response to be evaluated. Response versus time profiles of integrated or net responses and efficiency were examined. Rate of drug input has the greatest influence on the area under the effect curve when doses are larger and target drug concentrations are above the IC(50)/SC(50). The pharmacodynamics of drugs which elicit indirect pharmacologic responses such as corticosteroids, diuretics, growth hormone, erythropoietin and insulin indicate that sustained drug delivery enhances the therapeutic efficiency and pharmacodynamic availability.

Utilisation of pharmacokinetic-pharmacodynamic modelling and simulation in regulatory decision-making.

Modelling and simulation (M&S) play an important role in regulatory decision-making that affects both the public and industry. Technological advances in various fields related to drug development call for more focus on ways to optimise current drug development practices. Recognition of the potential of M&S by regulatory agencies inevitably has a substantial impact on drug development. The objective of the current review is to present the various regulatory initiatives for application of M&S to clinical drug development. The relevant parts of the various recommendations issued by the US Food and Drug Administration (FDA), via guidance documents and advisory committee meeting proceedings, are highlighted. Application of M&S to a variety of activities, such as integrating pharmacokinetic-pharmacodynamic knowledge across a new drug application and designing efficient trials, is discussed. Some of the challenges that pharmaceutical institutions currently face when implementing M&S projects, such as team structure, communication with regulators, training and time constraints, are also presented, and solutions are proposed.

Pharmacokinetic-pharmacodynamic modeling of rivastigmine, a cholinesterase inhibitor, in patients with Alzheimer's disease.

Rivastigmine is a cholinersterase inhibitor approved recently for the treatment of Alzheimer's disease (AD). The objective of this study is to characterize the pharmacokinetics-pharmacodynamics of rivastigmine in patients with AD. Eighteen AD patients received doses ranging from 1 to 6 mg bid for about 11 weeks. Rivastigmine and its active (major) metabolite (ZNS 114-666, also called NAP 226-90), plasma, and cerebrospinal fluid (CSF) concentrations were determined together with the AChE activity and computerized neuropsychological test battery (CNTB) scores. Nonlinear mixed-effects modeling of pharmacokinetic and pharmacodynamic data was conducted using NONMEM. Rivastigmine and its metabolite exhibited dose-disproportional pharmacokinetics. The apparent clearance and volume of distribution (plasma) of rivastigmine were estimated to be 120 L/h and 236 L, respectively. The relative bioavailability at the 6 mg dose was about 140%. The metabolite had a clearance of about 100 L/h and a volume of distribution of 256 L. The kinetics of the parent and metabolite in CSF showed an equilibration half-life of about 0.2 and 0.5 hours, respectively. The metabolite levels in CSF correlated very well with the acetylcholinesterase inhibition, with a ZNS 114-666 concentration of about 5.4 microg/L required for half-maximal inhibition of acetylcholinesterase activity. No statistically significant correlation of the CNTB scores with enzyme inhibition, parent or metabolite concentration (plasma/CSF), or rivastigmine dose could be established. The PK-PD model presented in this study can provide valuable information to optimize the drug development of rivastigmine and other related compounds and also in rationalizing dosing recommendations.

Quantitative structure-pharmacokinetic relationships (QSPR) of beta blockers derived using neural networks.

This study demonstrates the application of neural networks to predict the pharmacokinetic properties of beta-adrenoreceptor antagonists in humans. A congeneric series of 10 beta-blockers, whose critical pharmacokinetic parameters are well established, was selected for the study. An appropriate neural network system was constructed and tested for its ability to predict the pharmacokinetic parameters from the octanol/water partition coefficient (shake flask method), the pKa, or the fraction bound to plasma proteins. Neural networks successfully trained and the predicted pharmacokinetic values agreed well with the experimental values (average difference = 8%). The neural network-predicted values showed better agreement with the experimental values than those predicted by multiple regression techniques (average difference = 47%). Because the neural networks had a large number of connections, two tests were conducted to determine if the networks were memorizing rather than generalizing. The "leave-one-out" method verified the generalization of the networks by demonstrating that any of the compounds could be deleted from the training set and its value correctly predicted by the new network (average error = 19%). The second test involved the prediction of pharmacokinetic properties of compounds never seen by the network, and reasonable results were obtained for three out of four compounds tested. The results indicate neural networks can be a powerful tool in exploration of quantitative structure-pharmacokinetic relationships.

Artificial neural networks as a novel approach to integrated pharmacokinetic-pharmacodynamic analysis.

A novel model-independent approach to analyze pharmacokinetic (PK)-pharmacodynamic (PD) data using artificial neural networks (ANNs) is presented. ANNs are versatile computational tools that possess the attributes of adaptive learning and self-organization. The emulative ability of neural networks is evaluated with simulated PK-PD data, and the power of ANNs to extrapolate the acquired knowledge is investigated. ANNs of one architecture are shown to be flexible enough to accurately predict PD profiles for a wide variety of PK-PD relationships (e.g., effect compartment linked to the central or peripheral compartment and indirect response models). Also, an example is given of the ability of ANNs to accurately predict PD profiles without requiring any information regarding the active metabolite. Because structural details are not required, ANNs exhibit a clear advantage over conventional model-dependent methods. ANNs are proved to be robust toward error in the data and perturbations in the initial estimates. Moreover, ANNs were shown to handle sparse data well. Neural networks are emerging as promising tools in the field of drug discovery and development.

Influence of gender on prednisolone effects on whole blood T-cell deactivation and trafficking in rats.

Prednisolone (5 mg/kg intravenous) was administered to adrenalectomized male and female Sprague-Dawley rats (250-350 g) to assess the effects of gender on disposition and pharmacoimmunodynamics. Plasma concentrations of prednisolone were determined by high-performance liquid chromatography. Incorporation of [3H]thymidine (3H-TDR) was used to determine whole blood T-cell (WBTC) trafficking and deactivation following stimulation with Concanavalin-A. Whole blood T-cell trafficking was determined indirectly by using the glucocorticoid receptor antagonist RU-40555 (250 ng/mL) added to ex vivo cultures of whole blood from animals dosed with prednisolone. Mean (+/- SD) prednisolone clearance values were 3.22 +/- 0.88 and 3.46 +/- 0.96 L/h/kg in males and females, respectively. After administration of prednisolone, relative T-cell counts decreased slowly with time to reach a nadir at 3-5 h and returned to baseline levels by 8 h. Fitting data using an indirect response model yielded mean prednisolone 50% inhibitory concentration for inhibition of WBTC trafficking (IC50T) that was lower in males compared with females (0.14 +/- 0.16 versus 1.03 +/- 0.06 ng/mL; p < 0.05). In the absence of RU-40555, an immediate and complete inhibition of 3H-TDR incorporation into WBTC was observed (deactivation) and baseline levels were recovered slowly as prednisolone was cleared from blood. The mean 50% inhibitory concentration for inhibition of WBTC deactivation (IC50D) based on an inhibitory Imax model was similar in males and females (0.20 +/- 0.24 versus 0.18 +/- 0.12 ng/mL). Although male and female rats have similar exposure to prednisolone after 5-mg/kg doses, males are more sensitive to the inhibition of WBTC trafficking, whereas no gender effects on deactivation of WBTC exist.

Application of modeling and simulation to integrate clinical pharmacology knowledge across a new drug application.

Typical drug development includes few studies to find the right dose/dosing regimen and several other bridging studies evaluating various prognostic factors (e.g.: co-administration of other drugs, organ failure). The drug sponsors and the regulators use this information to formulate labeling instructions for safe and effective use of the drug. In the current article, modeling and simulation are proposed as tools to integrate the knowledge from the effectiveness/safety studies and the bridging studies. Simulations allow exploring the impact of various prognostic factors on the effectiveness and safety. The concept is exemplified using the new drug application of an anti-migraine drug. The exercise aids in integrating all the knowledge across the drug development to suggest rationale dosing strategies; effectively communicating the impact of the prognostic factors to the clinicians/regulators; and protect against any intellectual losses due to development team changes.

Should pharmacometrics be training the next R&D president?

We continue to train pharmacometric scientists primarily in methodology. The lack of training in business and drug development concepts, however, is preventing pharmacometricians from becoming high-level decision makers. The more recent growth of opportunities in pharmacometrics is propelled by applications within both companies and regulatory agencies. However, these applications themselves may not lead to sustained growth of opportunities. How can we prepare pharmacometricians to fundamentally re-engineer the drug development paradigm?

Exposure-response of posaconazole used for prophylaxis against invasive fungal infections: evaluating the need to adjust doses based on drug concentrations in plasma.

The purpose of this article is to report the exposure-response (E-R) relationship of posaconazole oral suspension (POS) for prophylaxis against invasive fungal infections (IFIs), on the basis of the US Food and Drug Administration (FDA) clinical pharmacology review of two randomized, active-controlled clinical studies. Posaconazole average steady state plasma concentrations (C(avg)) ranged from 22 to 3,650 ng/ml after administration of POS 200 mg three times daily (t.i.d.). In a double-blind, randomized clinical trial, the quartile ranges of C(avg) with midpoint values of 289, 736, 1,239, and 2,607 ng/ml had clinical failure rates of 44, 21, 18, and 18%, respectively, indicating an inverse association between C(avg) and clinical failure rate. There were no significant relationships between C(avg) and posaconazole-related major adverse events. Determining posaconazole concentrations in plasma will aid in assessing the need for either POS dose adjustment (e.g., increasing the POS dose) or switching to another systemic antifungal drug, thereby improving the effectiveness of prophylaxis against IFIs.

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.

Biomarkers in clinical drug development.

The primary purpose of this Commentary is to complement the description of biomarkers given in this issue in the Pharmaceutical Research and Manufacturers of America report by Lathia et al. I offer several examples of the use of biomarkers that highlight their value in drug development and regulatory decision making.

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.