Computational methodologies can be effective in predicting and prioritising key targets related to COVID

NovaLead Pharma, a Pune-based company has utilised its experience of drug repurposing has deployed computational technology to identify 42 existing drugs which may be potentially effective against the SARS-Cov2 virus. Supreet Deshpande, CEO, NovaLead Pharma shares more insights on how computational technologies can revolutionise pharma R&D and help gain significant advantages to tackle health threats like the coronavirus pandemic, in an exclusive interaction with Lakshmipriya Nair.

How can the current models of drug discovery and development be revamped to make pharma R&D more predictable, reliable, and less costly?

Pharma R&D can become more predictable, reliable, less costly and more productive by:

– Making smart use of the techniques and technologies in molecular sciences, coupled with data mining
– Leveraging the advancements in computational technologies, especially molecular simulations, neural networks, artificial intelligence and machine learning
– Using scientific algorithms for molecular library generation and virtual screening

What are the methods/strategies that can effectively predict or prioritise which targets to go after to treat or cure diseases which have emerged as a priority, especially as the Covid19 pandemic plays out?

Computational methodologies like molecular modelling and protein-ligand docking can be effective in predicting as well as prioritising key targets related to Covid19. Scientific literature is being made available about the important coronavirus targets (like spike proteins) as well the human proteins (like ACE2). Using computational homology models, drug molecules can be screened against these targets, to come out with useful hits. Genomic and proteomic analysis to understand biological pathways are important in this effort. For new viruses like SARS-CoV-2, where its proteins are not all known and even where they are known, their crystal structure may not be available so soon. In such cases, computational techniques of homology modelling are highly useful for drug screening. Accuracy of homology models depends on the underlying algorithms and expertise.

How are computational methodologies helping transform drug discovery and repurposing of existing drugs?   How can they be especially useful in the fight against zoonosis or infectious diseases?

Over the last two decades, computational technologies have been making huge inroads into drug discovery. Use of various structure-based, as well as ligand-based computational strategies, are being employed in the drug discovery process, by large global pharma companies as well as innovator drug discovery companies in the West. Drug repurposing is a relatively new field and there are not many innovator companies which use computational technologies to the fullest. NovaLead is one such innovator company, with its indigenously developed computational technology platform. The main challenge in infectious diseases is of pathogen mutations and new pathogens. A newly mutated virus or new virus may not have a potent vaccine or drug that effectively fights it. This scenario opens the potential for the rapid proliferation of the disease, potentially making into a pandemic. The recent COVID-19 is a case-in-point. Drug repurposing strategies with computational technologies can rapidly screen promising drugs against a range of viral and human targets, specific to the infectious diseases, and come up with the potential drug candidates. Being a known drug, such candidates can then be fast-tracked through the regulatory process to make into viable medicinal options to fight the disease.

Can you elaborate on a few computational methods and tools applied to predict or validate drugs’ efficacy with a couple of real-life examples?

Methods such as structure-based design, molecular docking, quantitative structure-activity relationship (QSAR), pharmacophore modelling are some of the methods used in predicting drug binding to selected protein targets. Above methods have been used in drug discovery of several successful drugs that have reached the market. Specific examples are anti-viral drugs such as Indinavir, Nelfinavir, Lopinavir, etc. where the above methods were used during the discovery of these drugs.

What are the challenges and opportunities that lie in developing and deploying analytic tools to transform complex and heterogeneous data into testable hypotheses and actionable insights?

The challenges in developing useful analytical tools include access to reliable and curated data, multi-disciplinary capabilities to develop problem-centric tools and limiting such tools to appropriate and optimum use. That said, due to advancements in AI/ML and cloud-based infrastructures, there are ample opportunities to find the niche and develop state-of-the-art analytical tools for scientific applications, especially for molecular-level analysis and predictions. However, it is not easy, as it requires real inter-disciplinary teams of scientists and technocrats to work in tandem, understand each other’s terminologies, rapidly test the hypothesis and correctly interpret the results. NovaLead in its own capability has proven that with the right grooming of talent and smart use of computational technologies, it is possible to develop an effective technology platform, which can become an engine for discovering repurposable drug candidates.

What are the steps that pharma companies need to take to generate and leverage data to improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments?

Use of modern technologies in research and development is essential for pharma companies to leverage available data and improve clinical outcomes. Better treatments can be designed and developed rapidly with the use of computational technologies for reliable and rapid prediction, trust-worthy prioritisation and informed go/no-go decisions.

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