Modeling and Simulation in Support of the Critical Path Initiative: Infrastructure, Challenges and Opportunities
by Daniel Weiner, PhD
Many in the pharmaceutical industry share FDA’s concerns about the slow translation of scientific discoveries into new and better medical treatments. The Critical Path Initiative, originally published in 2004, has now been supplemented by the "Critical Path National Opportunities List.” Both documents advocate increased use of drug-disease modeling and simulation to improve the efficiency of the drug product development process.
The systematic application of model-based drug development presupposes the ready availability of a PK/PD data infrastructure to support model building. When data are not readily available, the time needed to locate, clean, and prepare the data for analysis may cause delay in overall project execution and effective decision-making. The problem is growing as increases in the number of trials per development compound, the number of patients per trial, and the number of samples per patient are causing rapid escalation in PK data volumes.
FDA-Pharsight CRADA Collaboration
The FDA acknowledges these trends, and is assessing how to address the data management challenges it will face as it takes a more active role in the evaluation and creation of drug and disease models. To support continuing development of a technical infrastructure for model-based drug development, FDA’s Center for Drug Evaluation and Research (CDER) recently finalized a Cooperative Research and Development Agreement (CRADA) with Pharsight.
Under the terms of the CRADA collaboration, Pharsight will provide the FDA with software tools for the analysis, visualization, storage, reporting and review of PK/PD data. FDA will use these tools to review clinical trial data, especially for clinical pharmacology and clinical safety reviews. A key objective of the collaboration is to develop Pharsight Knowledgebase Server™ (PKS™) into a repository for the data needed for modeling and simulation, support Clinical Data Interchange Standards Consortium (CDISC) data formats, and interact with other FDA databases.
Dr. Robert Powell, director of FDA’s Division of Pharmacometrics, presented on the topic of FDA’s possible plans for a modern PK/PD data management architecture at the Pharsight PKS User Group meeting in October, 2005. In his speech delivered to PKS user companies representing 15 top pharmaceutical and biotech organizations, Dr. Powell discussed FDA’s need for a centralized clinical pharmacology data repository. Such a repository can be used to create a productive and validated workflow collecting all inputs and supporting all analyses. A repository addresses the problem of a lack of central management of the wealth of PK related knowledge generated in discovery, lead optimization and development, and how that knowledge can be optimized through modeling and simulation of clinical trial designs and clinical development programs. Pharsight will utilize the FDA’s feedback from the CRADA collaboration to further develop and customize PKS to meet industry data management and analysis requirements.
Slide presented by Bob Powell, Pharm.D., Director of Pharmacometrics, FDA, at 2005 Pharsight PKS User Group Meeting. Slide courtesy Bob Powell.
Several important challenges must be met to move forward with an integrated PK/PD data repository. The first is a profound adoption of standards. Without standards the value of a central data repository is severely limited. Standards must be adopted for data definitions, workflow, analysis, and reporting. The second principle is the use of automation to increase the efficiency of regulatory-compliant PK analysis. Because PK automation imposes a need for discipline across the organization, it requires a management mandate. However, once committed to, automation frees up quality assurance staff and increases the productivity of the pharmacokineticists who actually perform the work.
FDA and other companies are installing data repositories to house the information needed to support high productivity modeling and simulation (M&S). There has also been progress as CDISC promulgates the data standards needed to streamline the creation of models and facilitate their reuse.
Another technical enabler of modeling and simulation is software to enhance the capabilities of practitioners and engage key decision-makers. To enhance the productivity of M&S practitioners, current directions in technology must include flexible software that provides a seamless interface between source data, modeling software, databases containing modeling structure and results, simulation tools, and tools for report creation. To make M&S results and model-based inferences more accessible to decision-makers and other stakeholders requires user-friendly tools that allow non-modelers to explore model-based predictions of competing treatments and development strategies. Pharsight will take FDA’s feedback from the CRADA collaboration to further develop Drug Model Explorer™ (DMX®) to support Agency-sponsor interactions for the visualization and communication of model-based product profiles.
Beyond Technical Infrastructure
Management theory tells us that technical solutions alone are insufficient to create enduring change in the real world of complex organizations such as drug companies and FDA. It is encouraging therefore to see FDA and industry recognize investments in skills, organizational structure, and process that will be required to realize the full potential of modeling and simulation.
FDA has proposed a process for the incorporation of modeling and simulation into drug development planning. Specifically, FDA now advocates special meetings to be conducted at the end of phase IIa, where the sponsor and FDA can discuss modeling and simulation results early enough in the development program to reduce unnecessary failures in late stage clinical trials through nonbinding, scientific review of modeled exposure-response data from preclinical and clinical trials.
A related investment area in the M&S process is the commitment to model building as intellectual capital, with the development and updating of “standing” drug and disease models as a basis for therapeutic area knowledge management. For example, FDA is developing an internal library of therapeutically focused drug-disease models (e.g., in diabetes, Parkinson's) based on the literature and its own vast collection of clinical trial data from sponsor submissions. Proprietary source data from sponsors used to develop and validate the models would not be shared publicly, but FDA is considering how to begin sharing the models with industry1.
We are also seeing structural change in the major biopharmaceutical companies as they organize themselves to more systematically implement modeling and simulation. Full adoption of quantitative decision processes may require changes in how industry structures its R&D organization to bring together multi-disciplinary experts, facilitate communication across globally distributed teams, and assess the performance and impact of M&S. As industry gains more experience successfully applying modeling and simulation, organizational structures to align both technical and human M&S resources will continue to evolve.
Finally, adoption requires training of technical personnel and organizational leaders to execute modeling and simulation work. Effective execution itself requires a combination of biomedical knowledge and sophisticated quantitative skills that include probability and statistics. Alongside technical practitioners, M&S project leaders must have the full commitment of management and be able to effectively communicate with a broad range of organizational stakeholders, including physicians, statisticians, pharmacologists, and business and marketing personnel.
Finding enough people with the skill set needed to execute modeling and simulation work today continues to be a challenge. We have seen generous investment by companies such as Pfizer, which recently committed $4.5 million over three years to the University of Buffalo’s School of Pharmacy and Pharmaceutical Sciences to support expanded training and research in the field2. Additional training and professional development programs to build these skill sets are necessary, along with a critical examination of longer-term, peer-reviewed research funding for “Critical Path” M&S research.
The Path Forward
FDA’s move to increased reliance on proactive modeling and simulation has begun to drive trial designs and involve FDA and sponsors in more intensive communication about modeling assumptions and conclusions. The full potential of modeling and simulation will be realized when sponsor R&D management fully commits to implementation of data, workflow, and reporting standards, and builds state-of-the-art technical infrastructure for managing and communicating the growing volume of PK/PD data and modeling results. As part of its CRADA collaboration with FDA to support development of a model-based “Critical Path” infrastructure, Pharsight expects to test a new software platform to further integrate M&S technologies and facilitate definition and execution of supporting workflows. New platform technologies offer the potential to drive adoption of M&S through greater process efficiencies and lower barriers for practitioners to gain expertise.
Continued industry and FDA investments in the related skill sets, organizational capabilities, and delivery practices will be required to support systematic application of M&S to increase drug development productivity, improve the clinical quality and commercial performance of new therapies, and provide greater organizational transparency for decision-making.
1Powell R. FDA Experience with End of Phase IIa Meeting: An Attempt to Improve Drug Development Decisions. Presented at Clinical Pharmacology Subcommittee of the Advisory Committee for Pharmaceutical Sciences. November 14, 2005.
2 Press release from University of Buffalo, http://www.buffalo.edu/news/fast-execute.cgi/article-page.html?article=77560009.