The application of mathematical models to reflect the organization and activity of biological systems can be viewed as a continuum of purpose. The far left of the continuum is solely the prediction of biological parameter values, wherein an understanding of the underlying biological processes is irrelevant to the purpose. At the far right of the continuum are mathematical models, the purposes of which are a precise understanding of those biological processes. No models in present use fall at either end of the continuum. Without question, however, the emphasis in regards to purpose has been on prediction, e.g., clinical trial simulation and empirical disease progression modeling. Clearly the model that ultimately incorporates a universal understanding of biological organization will also precisely predict biological events, giving the continuum the logical form of a tautology. Currently that goal lies at an immeasurable distance. Nonetheless, the motive here is to urge movement in the direction of that goal. The distance traveled toward understanding naturally depends upon the nature of the scientific question posed with respect to comprehending and/or predicting a particular disease process. A move toward mathematical models implies a move away from static empirical modeling and toward models that focus on systems biology, wherein modeling entails the systematic study of the complex pattern of organization inherent in biological systems.

There is limited geriatrics‐oriented clinical pharmacological information available to guide pharmacotherapy in late‐life psychiatric disorders. In this paper, we review available data on interindividual differences in drug exposure and central nervous system functioning, amplified by drug–drug interactions in the elderly, that may contribute to variable responses to treatment and significant adverse drug effects. The inclusion of greater numbers of elderly persons in clinical trials and the vigorous application of clinical pharmacologic methodology (i.e., pharmacoepidemiology, population pharmacokinetic modeling, and pharmacogenetics) will be critical for improving safety and personalization of drug and dose selection for elderly patients. Clinical Pharmacology & Therapeutics (2008); 85, 1, 89–93 doi:

Quantitative assessments of tumor burden and modeling of longitudinal growth could improve phase II oncology trials. To identify obstacles to wider use of quantitative measures we obtained recorded linear tumor measurements from three published lung cancer trials. Model‐based parameters of tumor burden change were estimated and compared with similarly sized samples from separate trials. Time‐to‐tumor growth (TTG) was computed from measurements recorded on case report forms and a second radiologist blinded to the form data. Response Evaluation Criteria in Solid Tumors (RECIST)‐based progression‐free survival (PFS) measures were perfectly concordant between the original forms data and the blinded radiologist re‐evaluation (intraclass correlation coefficient = 1), but these routine interrater differences in the identification and measurement of target lesions were associated with an average 18‐week delay (range, −20 to 55 weeks) in TTG (intraclass correlation coefficient = 0.32). To exploit computational metrics for improving statistical power in small clinical trials will require increased precision of tumor burden assessments.