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Predictions, Modelling and Simulations: Their role in R&D decision-making is more “what” than “how”?

Posted on August 8th, 2016 by in Pharma R&D

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“Predictions are difficult, especially when they are about the future”, a statement attributed to the Noble laurate Niels Bohr is very true.  Despite these difficulties, the Pharma industry has built a range of predictive models to understand a range of drug actions including toxicity, disposition, elimination and efficacy. In Silico methods and modelling can also be used to target affinity and the selectivity in the design of new molecules.  Additionally, modelling can find new actions for old medicines.

Predictive modelling and simulations have been widely taken up in the craft of R&D and a wealth of research has led to the development of a range of service companies to support the R&D process.  This can be truly cost effective in terms of those highly valuable commodities of both time and money.

The IMI are extending this approach to the interface of clinical trials and real world use of medicines.  The Get Real Program is bringing key stakeholders together to look and hopefully agree on ways to bring real life data into the development process. Predictive Modelling and simulation therefore is really quite successful across the R&D phases from starting with an idea to the use of the idea in real world settings.

The use of these approaches is really quite comprehensive, with focus on “what” we do.  One potential gap in the use of predictive models and simulations is on “how’ we do it. Simulations have been used extensively in other industries to hone skills and capabilities in dealing with a range of complex information in fast changing environments.  The hours spent in flight simulation dealing with imaginary issues is a real testament to the power of this approach in training and the professional development of pilots.

In an R&D perspective we need to make key decisions at a variety of phases such as commit to development, first time in human, committing to phase 3 development and filing an application. Recent data in 2014 (Hay et al., 2014 Nature 32: p40) suggests that the industry success rates have actually been quite similar over 4 studies from 1989 to 2011.  This of course may well be due to the long cycle times of drug development but it is interesting to note that success rates from Phase 3 to New Application are between 55-68%.  During this time there has been considerable change in how the industry actually conducts the research.  There have been several cycles of business models in this time. Shifts from fully integrated units to a more de-centralised model focussing on key areas of expertise, leveraging a host of service providers and risk share models with other companies.

We are a science led industry and decisions are made by people either individually or in groups. From my experience of working across organisational and cultural differences there are 4 key areas that shape how we make decisions.  They are the bias that we all have, the perception of the data, the context in which we make the decision and the trade-offs we make.  With the decentralisation of expertise and knowhow perhaps it is even more important to model how we make decisions.  Creating simulations to help us understand our own individual preferences in decision making and helping individuals attend to all the information being presented may not only help improve our success rates.  Understanding how we process information could improve safety and help prevent further disasters which have hit the industry in 2006 with the CD28 Antibody (Tegenero) and the FAAH inhibitor (Bial) earlier this year.


 

All opinions shared in this post are the author’s own.

Find out more about how small steps, such as greater data sharing and extending data searches beyond standard literature searches, can bring about huge improvements to pre-clinical R&D productivity. 

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