Transformative innovation in drug development

One of the most promising channels for doing so is in-silico modelling, both in early development and across the product lifecycle, including drug substance, drug product, and clinical trials.

In-silico modelling
Thanks to the availability of high-quality datasets and new strategies for data analysis, in silico approaches can streamline drug product development and reduce the risks associated with trial-and-error experimental methods. For example, computational models can be used to characterise drugs more accurately and predict the best path for development. They can be used to inform formulation development and clinical trial design, including dose selection and optimisation. They can support the evaluation of critical regulatory review considerations, including evaluation of in-silico absorption, distribution, metabolism, excretion, and pharmacokinetics (ADME-PK). They can identify process development and optimisation issues. They can accelerate stability determination. And they can aid in the development of life cycle plans in the post-approval setting.

As in all settings, data is knowledge, and knowledge is power – but only if it is actionable. Predictive modelling has the potential to aid in developing robust drug development and manufacturing platforms. However, realizing the full 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.

In-silico modelling has evolved from being a nice-to-have alternative to real-world data sources to a must-have tool for drug development. When informed by real-world data and guided by a deep understanding of the interplay between all aspects of drug development, in-silico modelling is positioned to accelerate the industry approach to drug development and clinical research.

Speed up and de-risk development
This report provides a framework for that understanding by outlining some of the processes that stand to gain the most from computational modelling and identifying the in-silico capabilities that can be used to accelerate and de-risk each phase of development. Some of the key modelling capabilities that will be discussed include:

• Predictive modelling for solubility and bioavailability enhancement
• Accelerated stability modelling for shelf life and packaging determination
• Materials science, compaction simulation, and process modeling
• ADME-PK modelling to predict the effect of API physicochemical properties and pharmacokinetics.

Learn more
Thermo Fisher Scientific capabilities, individually and collectively shorten development timelines, reduce R&D costs, and increase the probability of technical success across all stages of drug development.

This article was originally published in European Biotechnology Magazine Autumn Edition 2022.