Pharmacogenetics and responders to a therapy: theoretical background and practical problems

Virtual patients and sensitivity analysis of the Guyton model of blood pressure regulation: towards individualized models of whole-body physiology

Mathematical models that integrate multi-scale physiological data can offer insight into physiological and pathophysiological function, and may eventually assist in individualized predictive medicine. We present a methodology for performing systematic analyses of multi-parameter interactions in such complex, multi-scale models. Human physiology models are often based on or inspired by Arthur Guyton's whole-body circulatory regulation model. Despite the significance of this model, it has not been the subject of a systematic and comprehensive sensitivity study. Therefore, we use this model as a case study for our methodology. Our analysis of the Guyton model reveals how the multitude of model parameters combine to affect the model dynamics, and how interesting combinations of parameters may be identified. It also includes a "virtual population" from which "virtual individuals" can be chosen, on the basis of exhibiting conditions similar to those of a real-world patient. This lays the groundwork for using the Guyton model for in silico exploration of pathophysiological states and treatment strategies. The results presented here illustrate several potential uses for the entire dataset of sensitivity results and the "virtual individuals" that we have generated, which are included in the supplementary material. More generally, the presented methodology is applicable to modern, more complex multi-scale physiological models.

New insights on the relation between untreated and treated outcomes for a given therapy effect model is not necessarily linear

BACKGROUND AND OBJECTIVES

A relation between the size of treatment efficacy and severity of the disease has been postulated and observed as linear for a few therapies. We have called this relation the effect model. Our objectives were to demonstrate that the relation is general and not necessarily linear.

STUDY DESIGN AND SETTING

We extend the number of observed effect model. Then we established three numerical models of treatment activity corresponding to the three modes of action we have identified. Using these models, we simulated the relation.

RESULTS

Empirical evidence confirms the effect model and suggests that it may be linear over a short range of event frequency. However, it provides an incomplete understanding of the phenomenon because of the inescapable limitations of data from randomized clinical trials. Numerical modeling and simulation show that the real effect model is likely to be more complicated. It is probably linear only in rare instances. The effect model is general. It depends on factors related to the individual, disease and outcome.

CONCLUSION

Contrarily to common, assumption, since the effect model is often curvilinear, the relative risk cannot be granted as constant. The effect model should be taken into account when discovering and developing new therapies, when making, health care policy decisions or adjusting clinical decisions to the patient risk profile.

QxDB: a generic database to support mathematical modelling in biology

QxDB (quantitative x-modelling database) is a web-based generic database package designed especially to house quantitative and structural information. Its development was motivated by the need for centralized access to such results for development of mathematical models, but its usefulness extends to the general research community of both modellers and experimentalists. Written in PHP (Hyper Preprocessor) and MYSQL, the database is easily adapted to new fields of research and ported to Apache-based web servers. Unlike most existing databases, experimental and observational results curated in QxDB are supplemented by comments from the experts who contribute input to the database, giving their evaluations of experimental techniques, breadth of validity of results, experimental conditions, and the like, thus providing the visitor with a basis for gauging the quality (or appropriateness) of each item for his/her needs. QxDB can be easily customized by adapting the contents of the database table containing the descriptors that characterize each data record according to an informal ontology of the research domain. We will illustrate this adaptability of QxDB by presenting two examples, the first dealing with modelling in oncology and the second with mechanical properties of cells and tissues.

Planning of clinical trials

This article focuses on steps of planning clinical trials most relevant to the question the clinician asks and how this question is properly transformed in a design and a protocol. All steps are important for the data quality or the validity of the results. A clinical trial is an experiment aimed at testing an hypothesis regarding the efficacy of a given intervention on an event, symptom or impaired quality of life in patients with a defined condition and a particular profile. As such, it should meet the fundamentals of scientific discovery that guarantee causality between the observed difference and the intervention. All the planning components are thought according to these fundamentals.