Category | Small Molecule
Biopharma developers face many challenges that span beyond the typical resource and expertise constraints. Every challenge encountered can add risk to your timeline and product quality
In silico modeling, both in early development and across the product lifecycle, can streamline drug development and reduce the risks associated with trial-and-error experimental methods. Realizing the potential of the technology requires careful selection and application of in silico strategies and a deep understanding of how to interpret and derive the most valuable insights from the data.
The technologic and pharmacologic advances that have enabled researchers to take aim at previously untreatable diseases have contributed to an increase in the number of molecules in the development pipeline that are challenging and difficult to develop. As the formulation and manufacturing complexities have increased, competitive pressure to reduce development time and costs has escalated and the requirements for market access have grown more elaborate.
To improve the likelihood of clinical success and return on investment for investigational new drugs, pharma and biotech companies are seeking innovative ways to accelerate progress and reduce some of the inherent scientific, economic, and delivery risks. One of the most promising channels for doing so is in silico modeling. In silico modeling, both in early development and across the product lifecycle, can streamline drug development and reduce the risks associated with trial-and-error experimental methods thus save time and costs.
Among their many applications, computational models can be used to:
As in all settings, data is knowledge, and knowledge is power—but only if it is actionable. Realizing the full potential of predictive modeling technology requires careful selection and application of in silico strategies and a deep understanding of how to interpret and derive the most valuable insights from the data.
Our recent whitepaper, Advancing drug development using in silico modeling, provides a framework for that understanding by outlining some of the processes that stand to gain the most from computational modeling and identifying the in silico capabilities that can be used to accelerate and de-risk each phase of development. Among the key modeling capabilities discussed in the report are predictive modeling for solubility and bioavailability enhancement; accelerated stability modeling for shelf life and packaging determination; materials science, compaction simulation, and process modeling; and ADME-PK modeling to predict the effect of API physicochemical properties and pharmacokinetics. Individually and collectively, these capabilities shorten development timelines, reduce R&D costs, and increase the probability of technical success across all stages of drug development.
Download the whitepaper to learn how in silico modeling capabilities can streamline your drug development program..