Improving the Development and Use of Biologically Based Dose Response Models (BBDR) in Risk Assessment

Biologically based dose-response (BBDR) models predict health outcomes (response) resulting from the presence of a toxicant at a biological target (dose). The benefits of BBDR models are many, and research programs are increasingly focusing on mechanistic research to support model development; however, progress has been slow. Impediments to progress include the complexity of dose response modeling, the need for a multidisciplinary team and consistent funding support, and difficulty in identifying and extracting the needed data. Of immediate concern is the lack of transparency of published models to the supporting data and literature, difficulty in accessing model code and simulation conditions sufficient to allow independent replication of results, and absence of well-defined quality criteria. Suggestions are presented to improve the development and use of BBDR models in risk assessment and to address the above limitations. Examples from BBDR models for methylmercury neurotoxicity and 5-fluorouracil embryotoxicity are presented to illustrate the suggestions including what kinds of databases are needed to support model development and transparency, quality assurance for modeling, and how the internet can advance database development and collaboration within the biological modeling community.

[1]  C. Kimmel,et al.  Evaluation of biologically based dose-response modeling for developmental toxicity: a workshop report. , 2000, Regulatory toxicology and pharmacology : RTP.

[2]  A M Jarabek,et al.  The application of dosimetry models to identify key processes and parameters for default dose-response assessment approaches. , 1995, Toxicology letters.

[3]  Hugh A. Barton,et al.  Harmonization: Developing Consistent Guidelines for Applying Mode of Action and Dosimetry Information to Cancer and Noncancer Risk Assessment , 1998 .

[4]  D. Rice,et al.  Critical periods of vulnerability for the developing nervous system: evidence from humans and animal models. , 2000, Environmental health perspectives.

[5]  H J Clewell,et al.  Evaluation of the uncertainty in an oral reference dose for methylmercury due to interindividual variability in pharmacokinetics. , 1999, Risk analysis : an official publication of the Society for Risk Analysis.

[6]  F E Yates,et al.  Good manners in good modeling: mathematical models and computer simulations of physiological systems. , 1978, The American journal of physiology.

[7]  R. Kavlock,et al.  Quantitative mechanistically based dose-response modeling with endocrine-active compounds. , 1999, Environmental health perspectives.

[8]  Phillip Yates,et al.  The Desk Reference of Statistical Quality Methods , 2001, Technometrics.

[9]  F. E..  A Relational Model of Data Large Shared Data Banks , 2000 .

[10]  J. Bert Keats,et al.  Quality Planning and Analysis , 1981 .

[11]  M C Kohn,et al.  Achieving credibility in risk assessment models. , 1995, Toxicology letters.

[12]  M. Delp,et al.  Physiological Parameter Values for Physiologically Based Pharmacokinetic Models , 1997, Toxicology and industrial health.

[13]  Edward G. Schilling,et al.  Juran's Quality Handbook , 1998 .

[14]  Robert S. DeWoskin,et al.  Sampling Techniques for Data Audits , 1989 .

[15]  E. F. Codd,et al.  The Relational Model for Database Management, Version 2 , 1990 .