In silico Design of Next-Gen Biotherapeutics

Antibody Biotherapeutic
As biotherapeutics become more diverse, being able to quickly and effectively predict their properties has become increasingly important.

Biotherapeutics have exploded onto the pharmaceutical R&D scene over the past two decades. Indeed, some of the most successful treatments over this time have been biologics, vastly improving patient outcomes for previously intractable diseases. This is due to their specificity: antibodies and antibody-like entities allow researchers to target individual cells with unprecedented precision. Additionally, since proteins form the basis of these entities, teams are taking advantage of their modular structures to attack multiple epitopes and improve their efficacy across larger patient populations.

Due to this potential, organizations are increasingly shifting their pipelines to focus on novel biotherapeutic entities. Emerging technologies such as CAR-T therapies and CRISPR-Cas9 gene editing offer even more promise. Yet as the number of modalities grows, so too does the complexity for R&D. To make this opportunity a reality, organizations will need to open the door to modern in silico technologies.

Novel Antibody Biotherapeutics

Antibody therapeutics have emerged as the poster child for diversity in today’s R&D pipelines. Originally based on human immunoglobulin (Ig) G, modifications to this “basic” formula have led to projects exploring bispecifics, trispecifics, DARPins, diabodies, triabodies and even camel- and shark-based Ig proteins.

Each type offers their own advantages and disadvantages, but developing and optimizing these modalities remains a significant challenge. To be successful, candidates must be specific to one (or multiple targets) at clinical concentrations, not spark an immune response and possess adequate physicochemical properties (i.e., solubility or viscosity). Indeed, this even remains a challenge for the development of “traditional” monoclonal antibodies. Therefore, developing an understanding of protein-target interactions and identifying potential formulation issues early in the R&D process can greatly improve outcomes.

To this end, researchers are progressively turning towards in silico techniques to characterize and improve candidate performance. For example, calculations of ΔΔG can assess the impact of mutations, helping improve protein binding. Likewise, methods such as spatial charge mapping can identify at-risk regions for protein aggregation. These methods and more, can guide R&D, focusing expensive physical experimentation on projects with a greater likelihood to succeed.

CAR-T Therapies: Whole-cell Biotherapeutics

CAR-T cells are showing significant promise as a new area of study for treating cancers. These biotherapeutics take advantage of the body’s natural immune response to attack with extreme precision. In this case, researchers design custom chimeric antigen receptors (CARs) – specifically, the portion of the receptor which binds to the antigen called the single chain variable fragment (scFv) –  to help T cells identify and kill diseased cells based on specific targets, like CD19. During treatment, T cells are collected from the patient, genetically modified to produce CARs for the disease and reinfused back into the patient.

To date, the FDA has approved CAR-T therapy for certain types of lymphoma and other related blood cancers. Expanding its scope into other disease areas, however, remains a nontrivial challenge. Each scFv must be tailored precisely to each antigen; otherwise, the treatment could inadvertently target healthy tissues.

Again, in silico techniques can help researchers understand the nuances of the intermolecular interactions between their candidate scFv and its target. Researchers can dock these proteins together to better understand their mechanism of binding and optimize their performance. This, coupled with structural knowledge of new antigens, can significantly expand the scope of CAR-T therapies. It can open the door to new approaches to treating diseases as diverse as HIV and fungal infections like aspergillosis.

Advancing Biotherapeutics Design

The Life Science Modeling & Simulation track at the virtual 2020 BIOVIA Conference dives into the latest findings in both small and large molecule therapeutics design. This blog summarizes parts of a presentation by Dr. Anne Goupil-Lamy who covered recent examples in the literature demonstrating how BIOVIA Discovery Studio aids the creation of novel antibody modalities, CAR-T therapies, CRISPR-Cas9 editing and more.

 

Talks are available to stream on-demand through October 16

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Sean McGee

Sean McGee is the Technical Marketing Manager for the BIOVIA brand of Dassault Systèmes. He has spent his career exploring the application of computational techniques in chemistry, specializing in data science, machine learning, and molecular modeling and simulation. At BIOVIA, Sean oversees the strategic positioning and communication of BIOVIA's solutions for upstream R&D in the life sciences, bulk and specialty chemicals, and consumer goods industries.

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