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.

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 ...

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