Descriptive versus mechanistic modelling
- 1. REDUCE COST WITH IMPROVED QUALITY MINIMIZE TIME RESEARCH AND DISCOVERY DESCRIPTIVE VERSUS MECHANISTIC MODELLING BY SAYEDA SALMA 1ST M PHARM DEPT OF PHARMACEUTICS 1
- 2. INDEX • INTRODUCTION • OBJECTIVES • CONCEPT • DIFFERENCE BETWEEN DESCRIPTIVE AND MECHANISTIC MODELLING • EXAMPLES • CONCLUSION • REFERNCES 2
- 3. INTRODUCTION • The new major challenge that the pharmaceutical industry is facing in the discovery and development of new drugs is to reduce costs and time needed from discovery to market, while at the same time raising standards of quality. • If the pharmaceutical industry cannot find a solution to reduce both costs and time, then its whole business model will be jeopardized. • The market will hardly be able, even in the near future, to afford excessively expensive drugs, regardless of their quality. 3
- 4. In parallel to this growing challenge, technologies are also dramatically evolving, opening doors to opportunities never seen before. This standard way to discover new drugs is essentially by trial and error. The “new technologies” approach has given rise to new hope in that it has permitted many more attempts per unit time, increasing proportionally, however, also the number of errors. The development of models in the pharmaceutical industry is certainly one of the significant breakthroughs proposed to face the challenges of cost, speed, and quality, somewhat imitating what happens in the aeronautics industry. The concept, however, is that of adopting just another new technology , known as “modeling”. 4
- 5. OBJECTIVES The use of models in the experimental cycle to reduce cost and time and improve quality. Without models, the final purpose of an experiment was one single drug or its behavior, with the use of models, the objective of experiments will be the drug and the model at the same level. Improving the model will help understanding the experiments on successive drugs and improving the model’s ability will help to represent reality. 5
- 6. CONCEPT According to Breiman , there are two cultures in the use of statistical modeling to reach conclusions from data. The first culture, namely, the data modeling culture, assumes that the data are generated by a given stochastic data model. whereas the other, the algorithmic modeling culture, uses algorithmic models and treats the data mechanism as unknown. To understand the mechanism, the use of modeling concepts is essential. 6
- 7. The purpose of the model is essentially for that of translating the known properties about the black box as well as some new hypotheses into a mathematical representation. In this way, a model is a simplifying representation of the data- generating mechanism under investigation. The identification of an appropriate model is often not easy and may require thorough investigation. 7
- 8. DESCRIPTIVE VERSUS MECAHNISTIC MODELLING DIFFERENCE Click on the globe to start 8
- 9. DESCRIPTIVE MODELLING If the purpose is just to provide a reasonable description of the data in some appropriate way without any attempt at understanding the underlying phenomenon, that is, the data-generating mechanism, then the family of models is selected based on its adequacy to represent the data structure. The net result in this case is only a descriptive model of the phenomenon. These models are very useful for discriminating between alternative hypotheses but are totally useless for capturing the fundamental characteristics of a mechanism. 9
- 10. MECHANISTIC MODELLING • Whenever the interest lies in the understanding of the mechanisms of action, it is critical to be able to count on a strong collaboration between scientists, specialists in the field, and statisticians or mathematicians. • The former must provide updated, rich, and reliable information about the problem. • whereas the latter are trained for translating scientific information in mathematical models. MECHANISM OF ACTION STATISTICIANS SCIENTIST AND SPECIALIST IN THE FIELD 10
- 11. EXAMPLE • A first evaluation of the data can be done by running non-parametric statistical estimation techniques like, for example, the Nadaraya– Watson kernel regression estimate. • These techniques have the advantage of being relatively cost-free in terms of assumptions, but they do not provide any possibility of interpreting the outcome and are not at all reliable when extrapolating. • The fact that these techniques do not require a lot of assumptions makes them relatively close to what algorithm-oriented people try to do. 11
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- 13. • These techniques are essentially descriptive by nature and are useful for summarizing the data by smoothing them and providing interpolated values. • The fit obtained by using the Nadaraya–Watson estimate on the set of data previously introduced is represented by the dashed line figure. • This approach, although often useful for practical applications, does not quite agree with the philosophical goal of science, which is to understand a phenomenon as completely and generally as possible. • This is why a parametric mechanistic modeling approach to approximate the data-generating process must be used. 13
- 14. • After having used a (simple) model formulation with some plausible meaning and a behavior matching the observed data structure, the next step in the quest for a good model. • The investigation of tumor growth on which we concentrate in this chapter falls in fact into the broad topic of growth curve analysis, which is one of the most common types of studies in which non-linear regression functions are employed. • Note that different individuals may have different tumor growth rates, either inherently or because of environmental effects or treatment, This will justify the population approach . 14
- 15. EXAMPLE 2 • The growth rate of a living organism or tissue can often be characterized by two competing processes. • The net increase is then given by the difference between anabolism and catabolism, between the synthesis of new body matter and its loss. • Catabolism is often assumed to be proportional to the quantity chosen to characterize the size of the living being, namely, weight or volume, whereas anabolism is assumed to have an allometric relationship to the same quantity. 15
- 16. • These assumptions on the competing processes are translated into mathematics by the following differential equation: • where µ(t) represents the size of the studied system in function of time. Note that this equation can be reformulated as follows: 16
- 17. • The curve represented by this last equation is commonly named the Richards curve. • When K is equal to one, the Richards curve becomes the well-known logistic function. • If the allometric factor in the relationship representing the catabolism mechanism is small, that is, K tends to 0, then the differential equation becomes : 17
- 18. • The general solution of this differential equation is now given by, µ(t) = αexp(−exp(−γ(t − η))), and is called the Gompertz curve. 18
- 19. CONCLUSION • The exponential growth model can thus be now justified not only because, it fits well the data but also because it can be seen as a first approximation to the Gompertz growth model, which is endowed with a mechanistic interpretation, namely, competition between the catabolic and anabolic processes. 19
- 20. REFERENCES Computer applications in pharmaceutical research by john wiley and sons and sean ekins 2006. Internet sources 20
- 21. THANK YOU MOVING TOWARDS DEVELOPING , IMPROVEMENT AND MOST ADVANCED CONDITIONS FOR COMPUTER AIDED PHARMACEUTICAL RESEARCH. 21