Challenges in the transition to model-based development

Abstract

Practitioners of the art and science of pharmacometrics are well aware of the considerable effort required to successfully complete modeling and simulation activities for drug development programs. This is particularly true because of the current, ad hoc implementation wherein modeling and simulation activities are piggybacked onto traditional development programs. This effort, coupled with the failure to explicitly design development programs around modeling and simulation, will continue to be an important obstacle to the successful transition to model-based drug development. Challenges with timely data availability, high data discard rates, delays in completing modeling and simulation activities, and resistance of development teams to the use of modeling and simulation in decision making are all symptoms of an immature process capability for performing modeling and simulation.A process that will fulfill the promise of model-based development will require the development and deployment of three critical elements. The first is the infrastructure--the data definitions and assembly processes that will allow efficient pooling of data across trials and development programs. The second is the process itself--developing guidelines for deciding when and where modeling and simulation should be applied and the criteria for assessing performance and impact. The third element concerns the organization and culture--the establishment of truly integrated, multidisciplinary, and multiorganizational development teams trained in the use of modeling and simulation in decision-making. Creating these capabilities, infrastructure, and incentivizations are critical to realizing the full value of modeling and simulation in drug development.KEYWORDS: pharmacometrics, model-based development, real-time data assembly PERSONAL REFLECTIONSDuring my 2-year clinical pharmacology fellowship with Dr. Leinis Sheiner at the University of California, San Francisco, starting in 1980, my primary research project focused on an evaluation of the population pharmacokinetics (PKs) of procainamide and its metabolite, N-acetyl procainamide, using plasma samples left over from routine laboratory evaluations and timed urine collections in patients treated for arrhythmias.1 My experiences in one of the earliest applications of nonlinear mixed-effect modeling (NONMEM) left an indelible impression as to the potential value of population modeling, as well as the difficulties in successfully executing a population analysis.The project required that I keypunch data onto cards and submit the NONMEM runs via a card reader to access the Lawrence Livermore Laboratory mainframe. The NON-MEM Project Group had its research account on this mainframe, and Dr. Stuart Beal was responsible for monitoring expenditures for mainframe time. To conserve resources, I was asked to carefully select runs for submission and to submit jobs after 9:00 PM, when cheaper computing rates applied. Parsimony proved to be a difficult principle to adhere to, because punching data onto ...

Concepts and Challenges in Quantitative Pharmacology and Model-Based Drug Development

“…MBDD is a paradigm and a mindset which promotes the use of modeling to delineate the path and focus of drug development. Models in MBDD serve as both the instruments and the aims of drug development (21). These models use available data, information, and knowledge to their maximum to improve the efficiency of the drug development process.…”

“…The gap between these two approaches is not totally unexpected and cannot be attributed to the unawareness of MBDD alone. Challenges in the transition from "modelaided" to MBDD have been recognized (21,33,34). We will further analyze these challenges and propose changes to alleviate if not to overcome them.…”

Facilitating Compound Progression of Antiretroviral Agents via Modeling and Simulation

“…The benefit of M&S approaches integrated into both a drug development paradigm (Chien et al 2005;Grasela et al 2005) and the regulatory review process (Bhattaram et al 2005;Gobburu and Sekar 2002) were previously discussed and are reasonably well appreciated. Recently, Miller et al (2005) discussed case studies in which M&S approaches were used in decision making, showing both a preclinical model of behavioral activity to predict potency and time course of response in humans (candidate differentiation) and the planning of a phase IIa dose ranging and proof of concept trial in Alzheimer's disease (criteria for a go/no-go decision) based on disease progression modeling and clinical trial simulation.…”

The current role of model-based drug development

“…Since the emergence of MBDD, several excellent reviews have been published on this topic that mainly focus on the conceptual framework of MBDD [18][19][20][21][22]. In addition, there are various examples in the literature that describe some methodology for the application of MBDD including how data and information can be transformed into knowledge useful for decision making.…”