Game-changing predictive solution for clinical research

Oct 4, 2012 by Clinovo    2 Comments    Posted under: Events, Trends

I wanted to share the following article with you, borrowed from the Drug, Discovery & Development online magazine:

Drug development programs today have a 5% to 10% probability of success. Almost half of the failures are due to drug safety issues found very late in the clinical development process. The lack of improvement in outcomes, despite advances in technology and the near doubling of pharmaceutical R&D expenditures, highlights the need for novel approaches to drug development.

Currently, the identification of efficacy and safety risks for a lead compound primarily uses cell line and in vivo studies. Unfortunately, these experimental systems are black boxes that offer limited visibility into selected phenotypes and biomarkers and very little insight into the effects of a compound on important physiological pathways. Due to this lack of transparency into pathway effects, it is difficult to generate insights into system-level changes in the physiological network. This is often a reason for potential oversight of toxicity issues and incorrect assessment of efficacy.

In the era of molecularly targeted drugs that affect specific targets and pathways, developers must have insights into the off-pathway effects of drug candidates. Use of predictive methodologies that emulate human physiology to test the impact of the drug candidate prior to moving the drug into clinical testing is crucial to improve the drug development success rate. By predicting clinical outcomes early on, the success rate of drug development can dramatically be improved.

The development of a predictive system emulating disease physiology is feasible because of the massive amount of published reductionist information on signaling and metabolic pathway components and “omics” data coupled with advances in mathematical techniques and computing power. Coupling the massive library of published data to computing power enables researchers to connect the dots in a way not possible before and, therefore, predict clinical outcomes early on.

Predictive models offer the promise of predicting clinical outcomes early in the development process and give the ability to rationally construct efficacious therapies with lower potential for side effects. The large availability of data and information on the components of the biological networks and interactions has enabled the creation of such systems. This approach provides transparency to manipulate different pathways in the network and assay intermediate and endpoint biomarkers and disease phenotypes. The key criterion for deployment of such an approach is extensive validation of predictions with experimental studies.

About the author - Pradeep Fernandes has a background in semiconductor engineering and has applied the engineering approaches and technologies to create the Cellworks technology platforms. Shireen Vali has a background in molecular and cellular neurobiology and has worked extensively in developing the disease networks underlying complex multi-phenotype disorders.

Olivier ROTH
Marketing & Communication Coordinator at Clinovo

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