Computational modeling plays an increasingly important role in drug discovery (docking, molecular simulations, and systems biology) and to some extent in the development process (pharmacokinetics and toxicity).[1, 2, 3, 4] The approach is now well established as reliable, cost effective, and an important method to reduce test-animals.
The manufacturing and production of biologics are costly and highly complex processes, therefore experimental testing of every biological molecule of interest for novel effective therapies is not feasible. Hence, new computer-based models are needed to reduce the search space and prioritise experimental testing. These predictive computer models will help rationalise the process and make biologics drug discovery and development more streamlined. Unlike as for small molecules, molecular descriptors are less available for larger entities like biologics, and few take advantage of their three-dimensional information and flexibility.
Using expertise from molecular modeling, molecular simulation, and protein engineering help to developed new computer models to characterise engineerable features relevant for the therapeutic behaviour of biologics. The features estimated are associated to ADME-T endpoints – which can be used to enunciate simple rules to guide the design of biologics akin to the Lipinski rule for small molecules. QSAR studies are performed to predict the effect of biologic for several biological endpoints. These models are developed based on the data collected, molecular descriptors or other sources of information at the physiological level when available (phospholipidosis, steatosis, cholestasis). Moreover, Quantitative Gene Expression Activity Relationship is investigated to go beyond the traditional QSAR methods, based on chemical structure. Such models give the opportunity to predict the relationship between biologics and biological outcomes.
These new computational models can be used to screen a collection of therapeutic proteins cheaper, faster and with a guaranteed chance of success, setting a new breakthrough in eliminating poor drug candidates earlier in the drug discovery process.
 Klinke, D. J. “Enhancing the Discovery and Development of Immunotherapies for Cancer Using Quantitative and Systems Pharmacology: Interleukin-12 as a Case Study.” [In English]. Journal for Immunotherapy of Cancer 3 (Jun 16 2015)
 Paul, S. M., et al. “How to Improve R&D Productivity: The Pharmaceutical Industry’s Grand Challenge.” [In English]. Nature Reviews Drug Discovery 9, no. 3 (Mar 2010)
 van de Waterbeemd, H., et al. “Admet in Silico Modelling: Towards Prediction Paradise?” [In English]. Nature Reviews Drug Discovery 2, no. 3 (Mar 2003)
 Ghemtio, L., et al. “Recent Trends and Applications in 3d Virtual Screening.” [In eng]. Comb Chem High Throughput Screen 15, no. 9 (Nov 2012)
 Robinson-Mosher A, Chen JH, Way J, Silver PA. Designing Cell-Targeted Therapeutic Proteins Reveals the Interplay between Domain Connectivity and Cell Binding. Biophys J 2014; 107(10): 2456-66.