Performing an In Silico Repurposing of Existing Drugs by Combining Virtual Screening and Molecular Dynamics Simulation

Repurposing of SARS-CoV-2 compounds against Marburg Virus using MD simulation, mm/GBSA, PCA analysis, and free energy landscape

The significant mortality rate associated with Marburg virus infection made it the greatest hazard among infectious diseases. Drug repurposing using in silico methods has been crucial in identifying potential compounds that could prevent viral replication by targeting the virus's primary proteins. This study aimed at repurposing the drugs of SARS-CoV-2 for identifying potential candidates against the matrix protein VP40 of the Marburg virus. Virtual screening was performed where the control compound, Nilotinib, showed a binding score of -9.99 kcal/mol. Based on binding scores, hit compounds 9549298, 11960895, 44545852, 51039094, and 89670174 were selected that had a lower binding score than the control. Subsequent molecular dynamics (MD) simulation revealed that compound 9549298 consistently formed a hydrogen bond with the residue Gln290. This was observed both in molecular docking and MD simulation poses, indicating a strong and significant interaction with the protein. 11960895 had the most stable and consistent RMSD pattern exhibited in 100 ns simulation, while 9549298 had the most identical RMSD plot compared to the control molecule. MM/PBSA analysis showed that the binding free energy (ΔG) of 9549298 and 11960895 was lower than the control, with -30.84 and -38.86 kcal/mol, respectively. It was observed by the PCA (principal component analysis) and FEL (free energy landscape) analysis that compounds 9549298 and 11960895 had lesser conformational variation. Overall, this study proposed 9549298 and 11960895 as potential binders of VP40 MARV that can cause its inhibition, however it inherently lacks experimental validation. Furthermore, the study proposes in-vitro experiments as the next step to validate these computational findings, offering a practical approach to further explore these compounds' potential as antiviral agents.Communicated by Ramaswamy H. Sarma.

Repurposing of approved antivirals against dengue virus serotypes: an in silico and in vitro mechanistic study

Dengue is an emerging, mosquito-borne viral disease of international public health concern. Dengue is endemic in more than 100 countries across the world. However, there are no clinically approved antivirals for its cure. Drug repurposing proves to be an efficient alternative to conventional drug discovery approaches in this regard, as approved drugs with an established safety profile are tested for new indications, which circumvents several time-consuming experiments. In the present study, eight approved RNA-dependent RNA polymerase inhibitors of Hepatitis C virus were virtually screened against the Dengue virus polymerase protein, and their antiviral activity was assessed in vitro. Schrödinger software was used for in silico screening, where the compounds were passed through several hierarchical filters. Among the eight compounds, dasabuvir was finally selected for in vitro cytotoxicity and antiviral screening. Cytotoxicity profiling of dasabuvir in Vero cells revealed changes in cellular morphology, cell aggregation, and detachment at 50 μM. Based on these results, four noncytotoxic concentrations of dasabuvir (0.1, 0.25, 0.5, and 1 µM) were selected for antiviral screening against DENV-2 under three experimental conditions: pre-infection, co-infection, and post-infection treatment, by plaque reduction assay. Viral plaques were reduced significantly (p < 0.05) in the co-infection and post-infection treatment regimens; however, no reduction was observed in the pretreatment group. This indicated a possible interference of dasabuvir with NS5 RdRp, as seen from in silico interaction studies, translating into a reduction in virus plaques. Such studies reiterate the usefulness of drug repurposing as a viable strategy in antiviral drug discovery. In this drug repurposing study, dasabuvir, a known anti-hepatitis C drug, was selected through virtual screening and assessed for its anti-dengue activity.

Drug Repurposing at the Interface of Melanoma Immunotherapy and Autoimmune Disease

Cancer cells have a remarkable ability to evade recognition and destruction by the immune system. At the same time, cancer has been associated with chronic inflammation, while certain autoimmune diseases predispose to the development of neoplasia. Although cancer immunotherapy has revolutionized antitumor treatment, immune-related toxicities and adverse events detract from the clinical utility of even the most advanced drugs, especially in patients with both, metastatic cancer and pre-existing autoimmune diseases. Here, the combination of multi-omics, data-driven computational approaches with the application of network concepts enables in-depth analyses of the dynamic links between cancer, autoimmune diseases, and drugs. In this review, we focus on molecular and epigenetic metastasis-related processes within cancer cells and the immune microenvironment. With melanoma as a model, we uncover vulnerabilities for drug development to control cancer progression and immune responses. Thereby, drug repurposing allows taking advantage of existing safety profiles and established pharmacokinetic properties of approved agents. These procedures promise faster access and optimal management for cancer treatment. Together, these approaches provide new disease-based and data-driven opportunities for the prediction and application of targeted and clinically used drugs at the interface of immune-mediated diseases and cancer towards next-generation immunotherapies.

Computational Bioprospecting Guggulsterone against ADP Ribose Phosphatase of SARS-CoV-2

Coronavirus Disease-2019 (COVID-19) is a highly contagious disease caused by Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2). The World Health Organization (WHO) classified the disease a as global public health hazard on 11 March 2020. Currently, there are no adequate measures to combat viral infections, including COVID-19, and the medication guidelines for the management of COVID-19 are dependent on previous findings from SARS-CoV and MERS-CoV research. Natural products have achieved widespread acceptance around the world as a means of enhancing healthcare and disease prevention. Plants are a potential source of antiviral factors such as flavonoids, phenolic acids, terpenoids, and others. Some of these agents exhibit a broad spectrum of antiviral activity. This study aimed to screen herbal leads for possible inhibitors of the SARS-CoV-2 ADP Ribose Phosphatase enzyme (ARP). Guggulsterone was found to be highly stabilized within the active site of the viral ARP enzyme by molecular dynamic simulation with very little fluctuation throughout the simulation timeframe of 100 ns. Thus, guggulsterone can be further used to develop a safe and competent medication for evolving therapy against SARS-CoV-2 in post-preclinical and clinical trials.

Application of Computational Biology and Artificial Intelligence in Drug Design

Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.

Recent Advances in Application of Computer-Aided Drug Design in Anti-Influenza A Virus Drug Discovery

Influenza A is an acute respiratory infectious disease caused by the influenza A virus, which seriously threatens global human health and causes substantial economic losses every year. With the emergence of new viral strains, anti-influenza drugs remain the most effective treatment for influenza A. Research on traditional, innovative small-molecule drugs faces many challenges, while computer-aided drug design (CADD) offers opportunities for the rapid and effective development of innovative drugs. This literature review describes the general process of CADD, the viral proteins that play an essential role in the life cycle of the influenza A virus and can be used as therapeutic targets for anti-influenza drugs, and examples of drug screening of viral target proteins by applying the CADD approach. Finally, the main limitations of current CADD strategies in anti-influenza drug discovery and the field's future directions are discussed.

Enhancing autophagy in Alzheimer's disease through drug repositioning

Alzheimer's disease (AD) is one of the biggest human health threats due to increases in aging of the global population. Unfortunately, drugs for treating AD have been largely ineffective. Interestingly, downregulation of macroautophagy (autophagy) plays an essential role in AD pathogenesis. Therefore, targeting autophagy has drawn considerable attention as a therapeutic approach for the treatment of AD. However, developing new therapeutics is time-consuming and requires huge investments. One of the strategies currently under consideration for many diseases is "drug repositioning" or "drug repurposing". In this comprehensive review, we have provided an overview of the impact of autophagy on AD pathophysiology, reviewed the therapeutics that upregulate autophagy and are currently used in the treatment of other diseases, including cancers, and evaluated their repurposing as a possible treatment option for AD. In addition, we discussed the potential of applying nano-drug delivery to neurodegenerative diseases, such as AD, to overcome the challenge of crossing the blood brain barrier and specifically target molecules/pathways of interest with minimal side effects.

Drug repurposing for cancer therapy in the era of precision medicine

Drug repurposing refers to the identification of clinically approved drugs, with the known safety profiles and defined pharmacokinetic properties, to new indications. Despite the advances in oncology research, cancers are still associated with the most unmet medical needs. Drug repurposing has emerged as a useful approach for the search for effective and durable cancer treatment. It may also represent a promising strategy to facilitate precision cancer treatment and to overcome drug resistance. The repurposing of non-cancer drugs for precision oncology effectively extends the inventory of actionable molecular targets and thus increases the number of patients who may benefit from precision cancer treatment. In cancer types where genetic heterogeneity is so high that it is not feasible to identify strong repurposed drug candidates for standard treatment, the precision oncology approach offers individual patients access to novel treatment options. For repurposed candidates with low potency, a combination of multiple repurposed drugs may produce a synergistic therapeutic effect. Precautions should be taken when combining repurposed drugs with anticancer agents to avoid detrimental drug-drug interactions and unwanted side effects. New multifactorial data analysis and artificial intelligence methods are needed to untangle the complex association of molecular signatures influencing specific cancer subtypes to facilitate drug repurposing in precision oncology.

In Silico Screening of Available Drugs Targeting Non-Small Cell Lung Cancer Targets: A Drug Repurposing Approach

The RAS-RAF-MEK-ERK pathway plays a key role in malevolent cell progression in many tumors. The high structural complexity in the upstream kinases limits the treatment progress. Thus, MEK inhibition is a promising strategy since it is easy to inhibit and is a gatekeeper for the many malignant effects of its downstream effector. Even though MEK inhibitors are under investigation in many cancers, drug resistance continues to be the principal limiting factor to achieving cures in patients with cancer. Hence, we accomplished a high-throughput virtual screening to overcome this bottleneck by the discovery of dual-targeting therapy in cancer treatment. Here, a total of 11,808 DrugBank molecules were assessed through high-throughput virtual screening for their activity against MEK. Further, the Glide docking, MLSF and prime-MM/GBSA methods were implemented to extract the potential lead compounds from the database. Two compounds, DB012661 and DB07642, were outperformed in all the screening analyses. Further, the study results reveal that the lead compounds also have a significant binding capability with the co-target PIM1. Finally, the SIE-based free energy calculation reveals that the binding of compounds was majorly affected by the van der Waals interactions with MEK receptor. Overall, the in silico binding efficacy of these lead compounds against both MEK and PIM1 could be of significant therapeutic interest to overcome drug resistance in the near future.

Repurposing of Anticancer Stem Cell Drugs in Brain Tumors

Brain tumors in adults may be infrequent when compared with other cancer etiologies, but they remain one of the deadliest with bleak survival rates. Current treatment modalities encompass surgical resection, chemotherapy, and radiotherapy. However, increasing resistance rates are being witnessed, and this has been attributed, in part, to cancer stem cells (CSCs). CSCs are a subpopulation of cancer cells that reside within the tumor bulk and have the capacity for self-renewal and can differentiate and proliferate into multiple cell lineages. Studying those CSCs enables an increasing understanding of carcinogenesis, and targeting CSCs may overcome existing treatment resistance. One approach to weaponize new drugs is to target these CSCs through drug repurposing which entails using drugs, which are Food and Drug Administration-approved and safe for one defined disease, for a new indication. This approach serves to save both time and money that would otherwise be spent in designing a totally new therapy. In this review, we will illustrate drug repurposing strategies that have been used in brain tumors and then further elaborate on how these approaches, specifically those that target the resident CSCs, can help take the field of drug repurposing to a new level.

Computational methods directed towards drug repurposing for COVID-19: advantages and limitations

Novel coronavirus disease 2019 (COVID-19) has significantly altered the socio-economic status of countries. Although vaccines are now available against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a causative agent for COVID-19, it continues to transmit and newer variants of concern have been consistently emerging world-wide. Computational strategies involving drug repurposing offer a viable opportunity to choose a medication from a rundown of affirmed drugs against distinct diseases including COVID-19. While pandemics impede the healthcare systems, drug repurposing or repositioning represents a hopeful approach in which existing drugs can be remodeled and employed to treat newer diseases. In this review, we summarize the diverse computational approaches attempted for developing drugs through drug repurposing or repositioning against COVID-19 and discuss their advantages and limitations. To this end, we have outlined studies that utilized computational techniques such as molecular docking, molecular dynamic simulation, disease-disease association, drug-drug interaction, integrated biological network, artificial intelligence, machine learning and network medicine to accelerate creation of smart and safe drugs against COVID-19.

WaterMap and Molecular Dynamic Simulation-Guided Discovery of Potential PAK1 Inhibitors Using Repurposing Approaches

p21-Activated kinase 1 (PAK1) is positioned at the nexus of several oncogenic signaling pathways. Currently, there are no approved inhibitors for disabling the transfer of phosphate in the active site directly, as they are limited by lower affinity, and poor kinase selectivity. In this work, a repurposing study utilizing FDA-approved drugs from the DrugBank database was pursued with an initial selection of 27 molecules out of ∼2162 drug molecules, based on their docking energies and molecular interaction patterns. From the molecules that were considered for WaterMap analysis, seven molecules, namely, Mitoxantrone, Labetalol, Acalabrutinib, Sacubitril, Flubendazole, Trazodone, and Niraparib, ascertained the ability to overlap with high-energy hydration sites. Considering many other displaced unfavorable water molecules, only Acalabrutinib, Flubendazole, and Trazodone molecules highlighted their prominence in terms of binding affinity gains through ΔΔG that ranges between 6.44 and 2.59 kcal/mol. Even if Mitoxantrone exhibited the highest docking score and greater interaction strength, it did not comply with the WaterMap and molecular dynamics simulation results. Moreover, detailed MD simulation trajectory analyses suggested that the drug molecules Flubendazole, Niraparib, and Acalabrutinib were highly stable, observed from their RMSD values and consistent interaction pattern with Glu315, Glu345, Leu347, and Asp407 including the hydrophobic interactions maintained in the three replicates. However, the drug molecule Trazodone displayed a loss of crucial interaction with Leu347, which was essential to inhibit the kinase activity of PAK1. The molecular orbital and electrostatic potential analyses elucidated the reactivity and strong complementarity potentials of the drug molecules in the binding pocket of PAK1. Therefore, the CADD-based reposition efforts, reported in this work, helped in the successful identification of new PAK1 inhibitors that requires further investigation by in vitro analysis.

Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review

Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.

Targeting SARS-COV-2 non-structural protein 16: a virtual drug repurposing study

Non-Structural Protein 16 (nsp-16), a viral RNA methyltransferase (MTase), is one of the highly viable targets for drug discovery of coronaviruses including SARS-CoV-2. In this study, drug discovery of SARS-CoV-2 nsp-16 has been performed by a virtual drug repurposing approach. First, drug shape-based screening (among FDA approved drugs) with a known template of MTase inhibitor, sinefungin was done and best compounds with high similarity scores were selected. In addition to the selected compounds, 4 nucleoside analogs of anti-viral (Raltgravir, Maraviroc and Favipiravir) and anti-inflammatory (Prednisolone) drugs were selected for further investigations. Then, binding energies and interaction modes were found by molecular docking approaches and compouds with lower energy were selected for further investigation. After that, Molecular dynamics (MD) simulation was carried to test the potential selected compounds in a realistic environment. The results showed that Raltegravir and Maraviroc among other compounds can bind strongly to the active site of the protein compared to sinefungin, and can be potential candidates to inhibit NSP-16. Also, the MD simulation results suggested that the Maraviroc and Raltegravir are more effective drug candidates than Sinefungin for inhibiting the enzyme. It is concluded that Raltegravir and Maraviroc which may be used in the treatment of COVID-19 after Invitro and invivo studies and clinical trial for final confirmation of drug effectiveness. Communicated by Ramaswamy H. Sarma.

Computational identification of repurposed drugs against viruses causing epidemics and pandemics via drug-target network analysis

Viral epidemics and pandemics are considered public health emergencies. However, traditional and novel antiviral discovery approaches are unable to mitigate them in a timely manner. Notably, drug repurposing emerged as an alternative strategy to provide antiviral solutions in a timely and cost-effective manner. In the literature, many FDA-approved drugs have been repurposed to inhibit viruses, while a few among them have also entered clinical trials. Using experimental data, we identified repurposed drugs against 14 viruses responsible for causing epidemics and pandemics such as SARS-CoV-2, SARS, Middle East respiratory syndrome, influenza H1N1, Ebola, Zika, Nipah, chikungunya, and others. We developed a novel computational “drug-target-drug” approach that uses the drug-targets extracted for specific drugs, which are experimentally validated in vitro or in vivo for antiviral activity. Furthermore, these extracted drug-targets were used to fetch the novel FDA-approved drugs for each virus and prioritize them by calculating their confidence scores. Pathway analysis showed that the majority of the extracted targets are involved in cancer and signaling pathways. For SARS-CoV-2, our method identified 21 potential repurposed drugs, of which 7 (e.g., baricitinib, ramipril, chlorpromazine, enalaprilat, etc.) have already entered clinical trials. The prioritized drug candidates were further validated using a molecular docking approach. Therefore, we anticipate success during the experimental validation of our predicted FDA-approved repurposed drugs against 14 viruses. This study will assist the scientific community in hastening research aimed at the development of antiviral therapeutics.

Repurposing ibuprofen-loaded microemulsion for the management of Alzheimer's disease: evidence of potential intranasal brain targeting

Studies have shown the use of non-steroidal anti-inflammatory drugs, such as ibuprofen could reduce the risk of Alzheimer’s disease. The drug-repurposing strategy offers a bright opportunity for these patients. Intranasal administration through the olfactory pathway provides noninvasive and direct drug delivery to the target brain. A novel ibuprofen microemulsion was prepared, characterized and assessed the brain uptake in rats. The solubility of ibuprofen in various oils, surfactants, co-surfactants, and different ratios of surfactant/co-surfactant mixtures was screened and the phase diagrams were constructed. The colloidal particle size was 166.3 ± 2.55 nm and the zeta potential was −22.7 mV. Conductivity and dilution test identified an O/W type microemulsion with pH 4.09 ± 0.08. The rheological study showed a Newtonian flow behavior with cP 10.633 ± 0.603 (mPa⋅s). A steady drug release and linear permeation profiles were observed and showed a 90% permeation rate from the released drug. Ibuprofen microemulsion showed excellent stability in 3-months accelerated storage conditions, heating-cooling and freeze-thaw cycles, accelerated centrifugation, and 6- and 12-months long-term storage conditions. In vivo studies in rats further demonstrated a 4-fold higher brain uptake of ibuprofen from the microemulsion compared to the reference solution and nearly 4-fold and 10-fold higher compared to the intravenous and oral administrations. This study provides an exciting repurposing strategy and new administration route for the treatment of Alzheimer’s disease.

Drug Repositioning For Allosteric Modulation of VIP and PACAP Receptors

Vasoactive intestinal peptide (VIP) and pituitary adenylate cyclase-activating polypeptide (PACAP) are two neuropeptides that contribute to the regulation of intestinal motility and secretion, exocrine and endocrine secretions, and homeostasis of the immune system. Their biological effects are mediated by three receptors named VPAC1, VPAC2 and PAC1 that belong to class B GPCRs. VIP and PACAP receptors have been identified as potential therapeutic targets for the treatment of chronic inflammation, neurodegenerative diseases and cancer. However, pharmacological use of endogenous ligands for these receptors is limited by their lack of specificity (PACAP binds with high affinity to VPAC1, VPAC2 and PAC1 receptors while VIP recognizes both VPAC1 and VPAC2 receptors), their poor oral bioavailability (VIP and PACAP are 27- to 38-amino acid peptides) and their short half-life. Therefore, the development of non-peptidic small molecules or specific stabilized peptidic ligands is of high interest. Structural similarities between VIP and PACAP receptors are major causes of difficulties in the design of efficient and selective compounds that could be used as therapeutics. In this study we performed structure-based virtual screening against the subset of the ZINC15 drug library. This drug repositioning screen provided new applications for a known drug: ticagrelor, a P2Y12 purinergic receptor antagonist. Ticagrelor inhibits both VPAC1 and VPAC2 receptors which was confirmed in VIP-binding and calcium mobilization assays. A following analysis of detailed ticagrelor binding modes to all three VIP and PACAP receptors with molecular dynamics revealed its allosteric mechanism of action. Using a validated homology model of inactive VPAC1 and a recently released cryo-EM structure of active VPAC1 we described how ticagrelor could block conformational changes in the region of 'tyrosine toggle switch' required for the receptor activation. We also discuss possible modifications of ticagrelor comparing other P2Y12 antagonist - cangrelor, closely related to ticagrelor but not active for VPAC1/VPAC2. This comparison with inactive cangrelor could lead to further improvement of the ticagrelor activity and selectivity for VIP and PACAP receptor sub-types.

Drug Repurposing for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive type of breast cancer which presents a high rate of relapse, metastasis, and mortality. Nowadays, the absence of approved specific targeted therapies to eradicate TNBC remains one of the main challenges in clinical practice. Drug discovery is a long and costly process that can be dramatically improved by drug repurposing, which identifies new uses for existing drugs, both approved and investigational. Drug repositioning benefits from improvements in computational methods related to chemoinformatics, genomics, and systems biology. To the best of our knowledge, we propose a novel and inclusive classification of those approaches whereby drug repurposing can be achieved in silico: structure-based, transcriptional signatures-based, biological networks-based, and data-mining-based drug repositioning. This review specially emphasizes the most relevant research, both at preclinical and clinical settings, aimed at repurposing pre-existing drugs to treat TNBC on the basis of molecular mechanisms and signaling pathways such as androgen receptor, adrenergic receptor, STAT3, nitric oxide synthase, or AXL. Finally, because of the ability and relevance of cancer stem cells (CSCs) to drive tumor aggressiveness and poor clinical outcome, we also focus on those molecules repurposed to specifically target this cell population to tackle recurrence and metastases associated with the progression of TNBC.

Project IDentif.AI: Harnessing Artificial Intelligence to Rapidly Optimize Combination Therapy Development for Infectious Disease Intervention

In 2019/2020, the emergence of coronavirus disease 2019 (COVID‐19) resulted in rapid increases in infection rates as well as patient mortality. Treatment options addressing COVID‐19 included drug repurposing, investigational therapies such as remdesivir, and vaccine development. Combination therapy based on drug repurposing is among the most widely pursued of these efforts. Multi‐drug regimens are traditionally designed by selecting drugs based on their mechanism of action. This is followed by dose‐finding to achieve drug synergy. This approach is widely‐used for drug development and repurposing. Realizing synergistic combinations, however, is a substantially different outcome compared to globally optimizing combination therapy, which realizes the best possible treatment outcome by a set of candidate therapies and doses toward a disease indication. To address this challenge, the results of Project IDentif.AI (Identifying Infectious Disease Combination Therapy with Artificial Intelligence) are reported. An AI‐based platform is used to interrogate a massive 12 drug/dose parameter space, rapidly identifying actionable combination therapies that optimally inhibit A549 lung cell infection by vesicular stomatitis virus within three days of project start. Importantly, a sevenfold difference in efficacy is observed between the top‐ranked combination being optimally and sub‐optimally dosed, demonstrating the critical importance of ideal drug and dose identification. This platform is disease indication and disease mechanism‐agnostic, and potentially applicable to the systematic N‐of‐1 and population‐wide design of highly efficacious and tolerable clinical regimens. This work also discusses key factors ranging from healthcare economics to global health policy that may serve to drive the broader deployment of this platform to address COVID‐19 and future pandemics.