4 Ways HPC & AI can Accelerate Vaccine Development for COVID-19
By: Kiran Narayanan
Tags: Machine Learning, Artificial Intelligence, Data Science, AI, Cloud Computing

As of 10 September 2020, 211 vaccines for #CORONA are in different stages of development and 31 candidates are in one of the four stages of clinical testing. There is a high chance of failure during clinical testing, as was the case with the the AstraZeneca and Oxford’s vaccine trials 5 days ago [1]. There is an urgent need for a vaccine for #COVID19, and it is crucial that the development time is minimized. #HighPerformanceComputing (HPC) & #ArtificialIntelligence (AI) can aid the vaccine development process by accelerating the vaccine discovery phase, and by reducing risks and costs during pre-clinical and clinical trials by better predicting candidate compounds for the vaccine.

How vaccines work

Vaccines prevent a disease and are not a cure for the disease itself. Traditionally, vaccines are constituted of the same germ that is either weakened or killed, or a part of the same germ that causes the diseases. Once injected into the human body, the vaccine stimulates the immune system to produce antibodies. The person receiving the vaccine develops immunity to the disease without having the get the disease first.

Vaccine development is time consuming and very expensive

The pharmaceutical vaccine development cycle takes 12–15 years with a cost of 800 Million USD to 1 Billion USD. The rate of success is only 1.8%, making the process very risky.

Stages of Vaccine Development

To expedite COVID19 vaccine development, there have been unprecedented international alliances and funding. It is expected that the development process will be reduced to 12–18 months from the usual 12–15 years.

Discovery Stage (Lead Discovery & Optimization)

The two main stages are the discovery stage and development stage. In the discovery stage, millions of potential vaccine candidates called leads are screened. The screening is performed against multiple criteria, most central to which is the effect the lead has on the target. The target is the specific site in the human body which the drug must affect in order to cure a disease. At the end of the discovery phase, a few hundred leads are selected to go into pre-clinical trials.

Development Stage (Pre-Clinical & Clinical Trials)

Pre-clinical trials

Development starts with pre-clinical trials, which are trials before human testing can begin. Here, data is collected to support the feasibility and safety of the vaccine via iterative non-human testing. The toxic and pharamcological effects of the vaccine are evaluated. Four to five candidates from the pre-clinical trials make it to clinical trials, out of which one drug gets approved and is used.

Clinical trials

The clinical trials have 4 phases.

Clinical Phase I: A small study is performed on healthy people to evaluate the safety and immune response at different doses. This typically would take up to 2 years, but for COVID19 has been accelerated to 3 months.

Clinical Phase II: Studies on 100s of people are conducted to further evaluate safety, efficacy and dosage. This typically would take up to 2–3 years, but for COVID19 has been accelerated to 8 months.

Clinical Phase III: Studies are conducted on 1000s of people. Normally takes 2–4 years, but for COVID19 has been combined with Phase II.

Regulatory Review & Approval: Government agencies review the data from clinical trials and evaluate the applications for licensing, before approval. Typically takes 1–2 years, but will be done within a few months for COVID19.

Clinical Phase IV: Involves testing in the real-world after approval, and after release to public.

4 Ways HPC & AI help COVID-19 vaccine development

#1 HPC & AI can lead to faster screening of potential candidates for the vaccine during discovery stage

During the vaccine discovery stage, millions of potential candidates for the vaccine are screened. The objective of the screening process is to find the most suitable compounds for further development. To be a suitable candidate, the candidate compound must satisfy several criteria. One such criteria is the binding affinity of the compound to the target. The COVID-19 virus has a spike protein which binds to the human ACE2 enzyme found in the alveoli of the lung, which leads to the incubation of the virus in the human and eventually, the illness. Therefore, one of the main criterias for the vaccine is that the vaccine molecules bind to the target, i.e., the spike protein of the virus, with greater affinity compared to the ACE2 enzyme, so as to be able to neutralize the virus.

There are two types of screening that can be performed using HPC & AI:

Structure-based screening: If the 3D structure of the target protein or enzyme is known, structure based screening methods are used. Here, a large database of potential candidates are used in a high-throughput molecular docking study to find if they bind to the target with high affinity.

Ligand-based screening: If the 3D structure of the target is not known, ligand based screening methods are used. Quantitative structure-activity relationships that model the structural features of ligands that bind to the target along with bioactivity, are used for screening.

Example #1: Screening using Molecular Dynamics Simulations

Molecular dynamics (MD) simulations are used for fast screening of potential vaccine candidates against the target virus protein. In a molecular dynamics simulation [2], the dynamic behavior of atoms in a system are simulated using multi-particle Newtonian dynamics equations of motion, which are solved using iterative numerical methods. These simulations are computationally very expensive, and therefore use parallel processing techniques that enable executing or running a simulation on thousands of processors. Thousands of MD simulations of large molecular systems are conducted to study the interactions of the vaccine candidates with the virus spike protein. Without the use of HPC & parallel computing such calculations would not be feasible.

Example# 2: Screening using an AI-based approach

AI models can guide the candidate molecules to multiple specific objectives, so as to ensure that those molecules can still be effective after human metabolization. One can then choose candidates based on their scores of effectiveness.

Case Study: Insilico, Hong Kong, discovered 7 candidates for COVID-19 in 4 days [3]

Case Study: Novartis uses MD simulations to screen millions of compounds in 9 hrs

#2 HPC & AI can lower pre-clinical trial costs by predicting properties a-priori

Pre-clinical trials cost between $50-$100 Million (Source: Tufts CSDD). This is due to the costs of assays and human labor, each compound can take up to $3000 make and several thousands of compounds need to be tested. HPC & AI techniques can be used in the discovery stage to predict molecular properties before synthesis, and can help save the costs of assays and tests.

#3 HPC & AI can reduce failure rate in clinical trials using genetic studies

Genetic studies can increase the probability that therapeutic modulation of a target will yield a drug that is safe and effective in humans, i.e., decrease the rate of attrition during Phase II and III of clinical trials. In typical pharma drug development, most drugs under development fail to pass Phase II and Phase III of the clinical trials, out of which 50% fail due to the lack of efficiency and more than 25% due to toxicity levels [4]. The recent AstraZeneca vaccine development failure [1] was an example of toxicity levels being high (dangerous potential side effects were found in Phase III of clinical trials).

Genetic studies can unlock novel genotype-phenotype relationships. Following are the 2 possible approaches -

Approach#1

Identify a series of alleles that are associated with human trait and that could be used to derive dose-response curves at the time of vaccine candidate validation

Approach#2

Uncover biological pathways that are important in human disease, and then use a pathway-based approach to conduct high-throughput drug screening, and thereby validate vaccine candidate.

#4 AI can lead to fast 3D reconstruction of high resolution structural models of proteins using Cryo-EM data

Cryo-Electron Microscopy or Cryo-EM [5,6] is used to develop ultra-high resolution structural models of compounds and viruses. These models are then used to understand how potential vaccine candidates interact with the virus, specifically the spike protein. HPC and AI-based software such as CryoSparc and Relion are often used to rapidly reconstruct the massive volumes of cryo-EM data to produce 3D models.

Case Study: UT Austin and NIH use CryoSparc AI tools for 3D reconstruction of COVID-19 spike protein [7]

Conclusion

High Performance Computing & AI can significantly accelerate vaccine discovery time and can reduce the potential risk of failure and costs accociated with the the clinical testing of vaccines. This article explores only the tip of the iceberg. There is a massive global effort to develop a vaccine for COVID-19, and several HPC consortiums and partnerships, together with massive governmental grants for supercomputing has led to a rapid progress of the algorithms and applications in this field.

Get access to advanced AI workflows and HPC applications such as LAMMPS, Parabricks, CryoSparc, HipMer & Relion, that are easily deployable on a high-speed network of supercomputers worldwide!

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