Background:
Discovery and development of a new drug is a long lasting and expensive
journey that takes around 20 years from starting idea to approval and marketing of new medication.
Despite R&D expenditures have been constantly increasing in the last few years, the number of new
drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical
safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number
of in silico techniques are currently being used for an early stage evaluation/prediction of potential
safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the
development of a new drug.
Methods:
In the present review, we will analyse the early steps of the drug-discovery pipeline, describing
the sequence of steps from disease selection to lead optimization and focusing on the most common
in silico tools used to assess attrition risks and build a mitigation plan.
Results:
A comprehensive list of widely used in silico tools, databases, and public initiatives that can
be effectively implemented and used in the drug discovery pipeline has been provided. A few examples
of how these tools can be problem-solving and how they may increase the success rate of a drug
discovery and development program have been also provided. Finally, selected examples where the
application of in silico tools had effectively contributed to the development of marketed drugs or clinical
candidates will be given.
Conclusion:
The in silico toolbox finds great application in every step of early drug discovery: (i) target
identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each
of these steps has been described in details, providing a useful overview on the role played by in silico
tools in the decision-making process to speed-up the discovery of new drugs.
