In Silico Modelling Across the Life Sciences

Drug discovery is notoriously expensive – and complicated. Current approaches are not only voracious in their appetite for effort and resources, but often leave questions unanswered. Structure-based approaches often fail to take account of the complete environment that drugs will act in and merely aim for a single target. On the other hand, phenotype-led discovery efforts are hampered by the need to identify their exact modes and targets of action. Both approaches have their value, but are in need of improvement. In silico modeling for drug design promises to do just that – while opening up de novo drug discovery efforts for previously overlooked and unmet needs.
In silico approaches have been growing in relevance throughout the pharmaceutical industry, led by one key motivation: their ability to cut costs. This is particularly true for Computer-Aided Drug Design (CADD), which can accelerate all aspects of pre-clinical development – the stage during which most drug candidates fail. Simulating the interactions between molecules presents a much more attractive alternative to carrying out tests in wet labs. This explains the cornucopia of different methods that are available to do so: Docking, quantitative structure-activity relationship (QSAR) models, ligand-protein interaction simulations, interaction fingerprinting, and others. In silico models have become a mainstay in the drug design process. This article examines some of their successes and outlines future challenges.

Anti-tumor Nanoparticles

Nanomedicine is a growing area within pharma – with potential applications across a variety of areas. As with many novel therapeutic modalities, it has been extensively investigated for applications in the fight against cancer. Over two thirds of nanomedical research has been in the area of oncology – with over a dozen tumor-targeting nanoparticles approved by the FDA, beginning in 1995. Yet the majority of nanoparticle therapies fail during clinical stages – largely due to a failure to account for the role the physiological environment will play in determining the transportation, specificity, and activity of the therapeutics. 

Transport barriers remain the chief obstacle to the success of nanomedicine for tumors, with only 5% of the dosage reaching its intended target. In silico models can ameliorate many of these shortcomings – and have already done so. For example, passive delivery methods have already been shown to be advantageous by exploiting the leaky vasculature surrounding tumors – which can benefit nanoparticles that can escape through pores in these vessels, leading to their accumulation at the site of the cancer. A review in Nature supports the predictive and explanatory power of in silico modeling over in vitro and in vivo alternatives, maintaining that multi-scale in silico models will be critical to the future development of nanoparticles. 

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Understanding SARS-CoV-2

One of the currently most investigated viruses – SARS-CoV-2, the causative agent for the COVID-19 pandemic – has naturally played a key role in expanding our understanding of how in silico models can be used to understand disease. COVID-19 and the funding that is committed to eradicating it have reignited infectious disease research and rejuvenated the field of vaccinology, with innovation in the field progressing at a rapid pace. In silico models have so far been used to identify drugs that could limit the cytokine storm triggered by severe reactions to infection by SARS-CoV-2, one of the leading causes of mortality. Critically, the authors of the research study also found drugs that could enhance the cytokine storm, such as topoisomerase inhibitors used against cancer – indicating the potential for dangerous drug interactions with the disease. The study illustrates the value of in silico models for discovering physiological interactions that can enlighten our approach to pharmacokinetics and pharmacodynamics. 

Another consequence of COVID-19 that is often observed is acute kidney injury, although the process that brings this about is not well understood. Researchers have recently employed in silico models to investigate potential lung-kidney cross talk during infection, finding significant differences in the expression of three genes that could explain the phenomenon. Protein docking models have also been employed in the characterization of the ancestral history of the virus through comparisons of its spike protein and binding affinity with other host species, indicating high binding affinities in pangolins. Pangolins have been posited as a possible intermediate host between bats and humans. The viral protein already exhibited optimal binding for human targets, indicative of the speed with which the virus has evolved to exploit its current hosts. These studies highlight the value of computational methods in rapidly investigating high-priority questions as well as the versatility of the issues they can be employed against.

Anti-Malarial Drug Development

Malaria remains one of the most concerning disease epidemics worldwide with 241 million cases worldwide in 2020. However, over 95% of these cases are in Africa – a largely developing continent. This limits the prospects of producing new antimalarial agents. The process of doing so is expensive, and the market for them is largely poor. Malaria is caused by five parasites, although Plasmodium falciparum and Plasmodium vivax remain the most concerning and dangerous. Prevention remains the most cost-effective approach to reducing the burden of the disease, while treatment still relies largely on compounds driven from old herbal extracts with antiparasitic activity: Artemisinin and Quinine. 

Many of these agents face increasing drug resistance: in vivo models have even managed to replicate the evolution of resistance to artemisinin derivatives, with quinine co-resistance. In silico models promise to revolutionize antimalarial drug discovery, the burden for which usually falls on cash-restricted government and academic institutions. New in silico models have already shed light on new candidates and their antiplasmodial activity. Computational docking studies were later confirmed by in vivo investigations, which highlights the potential of more accessible technologies in solving high priority needs. Further investigation in searching for antimalarial agents will likely be underpinned by these advances – and more research is urgently required. It is important to remember that malaria is not just a regional problem – as global warming grows, so will the range of the mosquitoes that transmit the parasite.

Future Developments

It is immediately clear that in silico models have entered the mainstream of pharmaceutical, but also biological, investigation. Their applicability is universal across the life sciences. The examples we explored show their potential in providing cost-efficient solutions to problems that have long gone unanswered, such as malaria. Equally, they can also accelerate some of the highest priority areas in pharma – with oncology warranting continuous innovation. And, crucially, their power in providing answers for COVID-19 can prove life-saving particularly with the time savings they facilitate. As we see greater implementation of Artificial Intelligence across the pharmaceutical and medical industries, we expect that the power of in silico models will only become more evident.

Nick Zoukas, Former Editor, PharmaFEATURES

Join Proventa International’s Drug Discovery Biology Strategy Meeting in London to further explore the topic of in silico models and their applications in drug discovery. Participate in closed roundtable discussions facilitated by world-leading industry experts and stakeholders that aim to establish rigorous scientific discourse and partnership!