The process of research and development (R&D) in the pharmaceutical industry has become increasingly unproductive during the last few decades. One reason, among others, for this development is the diminishing level of performance reached by research technologies. In the following study the term 'performance' is limited to an output measurement which is reflected by the number of new drugs launched into the market by which therapeutic improvements can be realized. The purpose of this study is to analyze the decreasing performance of traditional technologies in order to partly explain the reduction in R&D productivity. Subsequently, the potential impact of new technologies upon research performance will be simulated by using System Dynamics. Broad-scale random screening is the main technological process traditionally used to discover chemical substances for new drugs. This study reveals the random screening can be adequately modelled by the statistical formula Poisson function. The function is used to calculate the probability of discovering new drugs. Empirical data from the German pharmaceutical industry from the 1950s onwards were out into the formula. The results show that the probability of discovering new drugs has decreased strongly by using random screening. Furthermore, the risk involved in research with random screening can be measured by Poisson distribution functions. In can be seen that risk has risen significantly since the 1950s. The Poisson formula also provides a formal framework for forecasting the impact of new technologies on the rate of drug recovery. The high potential performance of new biotechnologies, especially genetic engineering, could increase research success rates significantly. A System Dynamics model has been constructed in a prototype version to generate scenarios for future output rates. The high uncertainly in predicting research successes can be estimated by a best, a worst and an intermediate forecast based upon varying assumptions. The software application Vensim has been used for modelling and simulating. The model is partly based on hypothetical data and is, therefore, a first step towards forecasting the impact of genetic engineering on research performance in the pharmaceutical industry.

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Date created

• 1995

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• Document

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• Processing documentation available at: https://wiki.albany.edu/display/SCA/Processing+Ingested+Digital+Files

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• Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International

  
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12420ec6bd5f758d2b4dea59aabd75a9

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23d738ba88f8333bc39725f9cb5bd0b8

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b83f2ce2912343b559f967dd985da515

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Collection

System Dynamic Society Records

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• This collection is part of the University Archives