<scp>IDentif</scp>.<scp>AI</scp>: Rapidly optimizing combination therapy design against severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐Cov‐2) with digital drug development

Blasiak, Agata; Lim, Jackwee; Seah, Shirley Gek Kheng; Kee, Theodore; Remus, Alexandria; Chye, De Hoe; Wong, Pui San; Hooi, Lissa; Truong, Anh T. L.; Le, Nguyen; Chan, Conrad En Zuo; Desai, Rishi; Li, Yiyang; Hanson, Brendon J.; Chow, Edward K.; Ho, Dean

doi:10.1002/btm2.10196

Cited by 29 publications

(44 citation statements)

References 48 publications

(98 reference statements)

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“…The authors also suggested the utilization of this platform for COVID-19 intervention. Blasiak et al (2021) utilized IDentif.AI to evaluate over 530,000 drug combinations against the SARS-CoV-2 live virus collected from a patient sample. IDentif.AI identified the combination of remdesivir, ritonavir, and lopinavir as a potentially effective treatment against SARS-CoV-2 infection.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2

Prasad

Kumar

2021

Current Research in Pharmacology and Drug Discovery

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It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing, and development, and motivate researchers in harnessing AI potentials in the fight against COVID-19.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2

Prasad

Kumar

2021

Current Research in Pharmacology and Drug Discovery

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“…Kaletra has gone through extensive clinical trials for COVID-19 but shows limited efficacy by itself 31,32 . Recently, using a digital drug development approach combining artificial intelligence and experimental validation to screen drug combinations for potential combination therapy against SARS-CoV-2, it was determined that one of the most effective potential combinations was Remdesivir, Ritonavir and Lopinavir 33 . This synergy of Ritonavir and Lopinavir with Remdesivir may be due to the exonuclease inhibitory activity of these two drugs (Fig.…”

Section: Discussionmentioning

confidence: 99%

Combination of Antiviral Drugs to Inhibit SARS-CoV-2 Polymerase and Exonuclease as Potential COVID-19 Therapeutics

Wang

Sacramento

Jockusch

et al. 2021

Preprint

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SARS-CoV-2 has an exonuclease-based proofreader, which removes nucleotide inhibitors such as Remdesivir that are incorporated into the viral RNA during replication, reducing the efficacy of these drugs for treating COVID-19. Combinations of inhibitors of both the viral RNA-dependent RNA polymerase and the exonuclease could overcome this deficiency. Here we report the identification of hepatitis C virus NS5A inhibitors Pibrentasvir and Ombitasvir as SARS-CoV-2 exonuclease inhibitors. In the presence of Pibrentasvir, RNAs terminated with the active forms of the prodrugs Sofosbuvir, Remdesivir, Favipiravir, Molnupiravir and AT-527 were largely protected from excision by the exonuclease, while in the absence of Pibrentasvir, there was rapid excision. Due to its unique structure, Tenofovir-terminated RNA was highly resistant to exonuclease excision even in the absence of Pibrentasvir. Viral cell culture studies also demonstrate significant synergy using this combination strategy. This study supports the use of combination drugs that inhibit both the SARS-CoV-2 polymerase and exonuclease for effective COVID-19 treatment.

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“…The first permutation of IDentif.AI used neural networks to reveal that the biological response to therapy can be represented by a smooth surface. Subsequent studies resolved this surface, which can rapidly identify optimal combinations, using a second-order algebraic function, with its coefficients determined through a small number of prospective experiments (Abdulla et al, 2020;Al-Shyoukh et al, 2011;Blasiak et al, 2021;Clemens et al, 2019;Ho, 2020;Ho et al, 2020;Lee et al, 2017;Lim et al, 2020;Mohd Abdul Rashid et al, 2015;Rashid et al, 2018;Silva et al, 2016;Wang et al, 2015;Wong et al, 2008). This correlation has subsequently been verified in prospective, human studies in infectious disease, cancer therapy, transplant medicine, and other indications (Tan BKJ et al, 2021;Blasiak et al, 2020;de Mel et al, 2020;Kee et al, 2019;Pantuck et al, 2018;Shen et al, 2020;Zarrinpar et al, 2016).…”

Section: Introductionmentioning

confidence: 92%

“…RDV and EIDD-1931 were the only drugs that achieved EC50 < Cmax with Cmax /EC50 ratios of 2.92 and 11.46, respectively. RDV, RTV and LPV performance in monotherapy was comparable with that observed in the previous IDentif.AI study based on the same assay (Blasiak et al, 2021).…”

Section: Monotherapies Were Broadly Not Sufficiently Efficacious In the Actionable Dosing Rangementioning

confidence: 99%

“…Instead, it uses experimental assays to determine the drugs and doses that constitute globally optimized combination regimens. Our previous IDentif.AI studies pinpointed top-ranked combinations (based on inhibition efficacy) mediated by unforeseen drug interactions (Blasiak et al, 2021). Following this study, we developed IDentif.AI 2.0 to expand the resolution of drug and dose analysis to yield a broader spectrum of clinically actionable combinations.…”

Section: Introductionmentioning

confidence: 99%

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The IDentif.AI 2.0 Pandemic Readiness Platform: Rapid Prioritization of Optimized COVID-19 Combination Therapy Regimens

Blasiak

Truong

Remus

et al. 2021

Preprint

Self Cite

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Objectives: We aimed to harness IDentif.AI 2.0, a clinically actionable AI platform to rapidly pinpoint and prioritize optimal combination therapy regimens against COVID-19. Methods: A pool of starting candidate therapies was developed in collaboration with a community of infectious disease clinicians and included EIDD-1931 (metabolite of EIDD-2801), baricitinib, ebselen, selinexor, masitinib, nafamostat mesylate, telaprevir (VX-950), SN-38 (metabolite of irinotecan), imatinib mesylate, remdesivir, lopinavir, and ritonavir. Following the initial drug pool assessment, a focused, 6-drug pool was interrogated at 3 dosing levels per drug representing nearly 10,000 possible combination regimens. IDentif.AI 2.0 paired prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus (propagated, original strain and B.1.351 variant) and Vero E6 assay with a quadratic optimization workflow. Results: Within 3 weeks, IDentif.AI 2.0 realized a list of combination regimens, ranked by efficacy, for clinical go/no-go regimen recommendations. IDentif.AI 2.0 revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived. Conclusions: IDentif.AI 2.0 rapidly revealed promising drug combinations for a clinical translation. It pinpointed dose-dependent drug synergy behavior to play a role in trial design and realizing positive treatment outcomes. IDentif.AI 2.0 represents an actionable path towards rapidly optimizing combination therapy following pandemic emergence.

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IDentif.AI: Rapidly optimizing combination therapy design against severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐Cov‐2) with digital drug development

Cited by 29 publications

References 48 publications

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2

Combination of Antiviral Drugs to Inhibit SARS-CoV-2 Polymerase and Exonuclease as Potential COVID-19 Therapeutics

The IDentif.AI 2.0 Pandemic Readiness Platform: Rapid Prioritization of Optimized COVID-19 Combination Therapy Regimens

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