The challenges of decision-making arise from the importance of multiple/conflicting criteria to the success of a potential drug molecule, the large amount of data generated and the inherent uncertainty in that data. The study illustrates how biased decisions in drug discovery can result in scientists missing good compounds by not searching widely enough and wasting resources by clinging to ideas that should be dismissed. The new paper: "Overcoming Psychological Barriers to Good Discovery Decisions" is published in Drug Discovery Today (2010) Volume 15, Numbers 13/14_July 2010.
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The paper identifies that while company rules, processes and systems seek to foster objectivity, common biases in decision making may hinder achievement of best possible performance. Psychological research proves again and again that people are bad at making complex decisions where there is risk involved. The paper demonstrates how better individual and team decision-making within drug discovery would enhance R&D performance.
Feedback on problem solving performance could be one of the simplest measures to improve selection of compounds and effective screening sequences. The study also highlights that computational tools provide a more scientific approach that encourages objective consideration of all of the available information, helping scientists make decisions that are both balanced and rational, in the domains of library design, compound selection, screening, profiling and experimental design.
Drug discovery leaders receive much conflicting advice on possible ways to improve productivity and restore the rate of successful drug launches.
Continuing technology investment, outsourcing of shared services and formation of smaller, disease-specific units, which bring researchers closer to clinicians are all current trends. However, senior management cannot afford to ignore the human dimension - are their teams making the best possible decisions given the information available to them, or that could be available given the right experiments?
Dr. Andrew Chadwick, Principal Consultant, Tessella, explains, "Past experience shows that many practical researchers remain baffled or confused by probabilistic models and so shy away from formal decision analysis. Yet reliance on gut instinct tends to lead to consistent patterns of mistakes. Discovery groups need to define and encourage 'best practice' to conduct projects in a way that captures wider company and industry experience. There is a need to make this as simple and accessible as possible via a more scientific approach."