Systematic indication extension for drugs using patient stratification insights generated by combinatorial analytics

Das, Sayoni; Taylor, Krystyna; Beaulah, Simon; Gardner, Steve

doi:10.1016/j.patter.2022.100496

Cited by 6 publications

(5 citation statements)

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“…Using a systematic repositioning approach [ 68 ], we identified 13 long COVID targets that already have drugs in clinical development. As these drugs or development candidates may require fewer preclinical studies and already have a known safety profile, they could represent a quicker and de-risked strategy for developing potential new treatments.…”

Section: Resultsmentioning

confidence: 99%

Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis

Taylor,

Pearson,

Das

et al. 2023

J Transl Med

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Background Long COVID is a debilitating chronic condition that has affected over 100 million people globally. It is characterized by a diverse array of symptoms, including fatigue, cognitive dysfunction and respiratory problems. Studies have so far largely failed to identify genetic associations, the mechanisms behind the disease, or any common pathophysiology with other conditions such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) that present with similar symptoms. Methods We used a combinatorial analysis approach to identify combinations of genetic variants significantly associated with the development of long COVID and to examine the biological mechanisms underpinning its various symptoms. We compared two subpopulations of long COVID patients from Sano Genetics’ Long COVID GOLD study cohort, focusing on patients with severe or fatigue dominant phenotypes. We evaluated the genetic signatures previously identified in an ME/CFS population against this long COVID population to understand similarities with other fatigue disorders that may be triggered by a prior viral infection. Finally, we also compared the output of this long COVID analysis against known genetic associations in other chronic diseases, including a range of metabolic and neurological disorders, to understand the overlap of pathophysiological mechanisms. Results Combinatorial analysis identified 73 genes that were highly associated with at least one of the long COVID populations included in this analysis. Of these, 9 genes have prior associations with acute COVID-19, and 14 were differentially expressed in a transcriptomic analysis of long COVID patients. A pathway enrichment analysis revealed that the biological pathways most significantly associated with the 73 long COVID genes were mainly aligned with neurological and cardiometabolic diseases. Expanded genotype analysis suggests that specific SNX9 genotypes are a significant contributor to the risk of or protection against severe long COVID infection, but that the gene-disease relationship is context dependent and mediated by interactions with KLF15 and RYR3. Comparison of the genes uniquely associated with the Severe and Fatigue Dominant long COVID patients revealed significant differences between the pathways enriched in each subgroup. The genes unique to Severe long COVID patients were associated with immune pathways such as myeloid differentiation and macrophage foam cells. Genes unique to the Fatigue Dominant subgroup were enriched in metabolic pathways such as MAPK/JNK signaling. We also identified overlap in the genes associated with Fatigue Dominant long COVID and ME/CFS, including several involved in circadian rhythm regulation and insulin regulation. Overall, 39 SNPs associated in this study with long COVID can be linked to 9 genes identified in a recent combinatorial analysis of ME/CFS patient from UK Biobank. Among the 73 genes associated with long COVID, 42 are potentially tractable for novel drug discovery approaches, with 13 of these already targeted by drugs in clinical development pipelines. From this analysis for example, we identified TLR4 antagonists as repurposing candidates with potential to protect against long term cognitive impairment pathology caused by SARS-CoV-2. We are currently evaluating the repurposing potential of these drug targets for use in treating long COVID and/or ME/CFS. Conclusion This study demonstrates the power of combinatorial analytics for stratifying heterogeneous populations in complex diseases that do not have simple monogenic etiologies. These results build upon the genetic findings from combinatorial analyses of severe acute COVID-19 patients and an ME/CFS population and we expect that access to additional independent, larger patient datasets will further improve the disease insights and validate potential treatment options in long COVID.

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Section: Resultsmentioning

confidence: 99%

Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis

Taylor,

Pearson,

Das

et al. 2023

J Transl Med

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“…Possui um portfólio de serviços também estruturados em desenvolvimento e caracterização (por meio de protocolos de IA) de fármacos candidatos para o tratamento de doenças. Resultados apresentados pela Precisionlife revelaram o potencial de centenas de medicamentos (447) já registrados e comercializados por 177 empresas biofarmacêuticas, respondendo às necessidades de inúmeros pacientes e disponibilizando receita adicional de vários bilhões de dólares às empresas detentoras dos direitos das patentes destes fármacos (DAS;BEAULAH;GARDNER, 2022). Em parceria recente com uma empresa (SanoGenetics) detentora de dados genéticos de 3.000 indivíduos adultos que sofrem de sintomas prolongados de COVID 19, a Precisionlife pretende nos próximos anos identificar fatores de risco e potenciais medicamentos-alvo para o tratamento de pacientes que se enquadrem nesse contexto.…”

Section: Resultsunclassified

Prospecção Tecnológica sobre o Setor de Plataformas de Inteligência Artificial Aplicadas ao Reposicionamento de Drogas Contra a COVID-19

Melo

Rocha

2023

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Em um mundo pós-pandemia, em que as cadeias de transmissão do vírus SARS-CoV-2 ainda se farão presentes, é clara a necessidade do domínio dos algoritmos de aprendizado de máquina em sistemas de inteligência artificial com o objetivo de testar a reutilização de drogas já existentes contra a COVID-19. Para prospectar a produção científico-tecnológica sobre o tema, foi realizada uma busca do termo repurposing AND drugs AND machine learning AND COVID nas bases Web of Science, Orbit e Lens (2017 a 2022). Foram identificados 71 registros bibliográficos com autores estruturados em dois grupos, sendo o segundo detentor dos documentos mais recentes. Foram identificadas nove classes de IPCs com os principais domínios tecnológicos relacionados ao tema e todos distribuídos em 42 documentos ativos de patentes. Destes, quatro se encontravam concedidos às empresas “RO5” e “Precisionlife”. Das 50 startups mais promissoras de 2022, apenas duas desenvolvem esse tipo de tecnologia, o que reforça o entendimento sobre o número ainda pequeno de players no setor e evidencia o horizonte promissor para essa área de produção de tecnologia.

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“…Using a systematic repositioning approach 67 , we identified 13 long COVID targets that already have drugs in clinical development. As these drugs or development candidates may require fewer preclinical studies and already have a known safety profile, they could represent a quicker and de-risked strategy for developing potential new treatments.…”

Section: Evaluation Of Potential Novel Drug Targets and Repurposing O...mentioning

confidence: 99%

Genetic Risk Factors for Severe and Fatigue Dominant Long COVID and Commonalities with ME/CFS Identified by Combinatorial Analysis

Taylor¹,

Pearson²,

Das³

et al. 2023

Preprint

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Background Long COVID is a debilitating chronic condition that has affected over 100 million people globally. It is characterized by a diverse array of symptoms, including fatigue, cognitive dysfunction and respiratory problems. Studies have so far largely failed to identify genetic associations, the mechanisms behind the disease, or any common pathophysiology with other conditions such as ME/CFS that present with similar symptoms. Methods We used a combinatorial analysis approach to identify combinations of genetic variants significantly associated with the development of long COVID and to examine the biological mechanisms underpinning its various symptoms. We compared two subpopulations of long COVID patients from Sano Genetics' Long COVID GOLD study cohort, focusing on patients with severe or fatigue dominant phenotypes. We evaluated the genetic signatures previously identified in an ME/CFS population against this long COVID population to understand similarities with other fatigue disorders that may be triggered by a prior viral infection. Finally, we also compared the output of this long COVID analysis against known genetic associations in other chronic diseases, including a range of metabolic and neurological disorders, to understand the overlap of pathophysiological mechanisms. Results Combinatorial analysis identified 73 genes that were highly associated with at least one of the long COVID populations included in this analysis. Of these, 9 genes have prior associations with acute COVID-19, and 14 were differentially expressed in a transcriptomic analysis of long COVID patients. A pathway enrichment analysis revealed that the biological pathways most significantly associated with the 73 long COVID genes were mainly aligned with neurological and cardiometabolic diseases. Expanded genotype analysis suggests that specific SNX9 genotypes are a significant contributor to the risk of or protection against severe long COVID infection, but that the gene-disease relationship is context dependent and mediated by interactions with KLF15 and RYR3. Comparison of the genes uniquely associated with the Severe and Fatigue Dominant long COVID patients revealed significant differences between the pathways enriched in each subgroup. The genes unique to Severe long COVID patients were associated with immune pathways such as myeloid differentiation and macrophage foam cells. Genes unique to the Fatigue Dominant subgroup were enriched in metabolic pathways such as MAPK/JNK signaling. We also identified overlap in the genes associated with Fatigue Dominant long COVID and ME/CFS, including several involved in circadian rhythm regulation and insulin regulation. Overall, 39 SNPs associated in this study with long COVID can be linked to 9 genes identified in a recent combinatorial analysis of ME/CFS patient from UK Biobank. Among the 73 genes associated with long COVID, 42 are potentially tractable for novel drug discovery approaches, with 13 of these already targeted by drugs in clinical development pipelines. From this analysis for example, we identified TLR4 antagonists as repurposing candidates with potential to protect against long term cognitive impairment pathology caused by SARS-CoV-2. We are currently evaluating the repurposing potential of these drug targets for use in treating long COVID and/or ME/CFS. Conclusion This study demonstrates the power of combinatorial analytics for stratifying heterogeneous populations in complex diseases that do not have simple monogenic etiologies. These results build upon the genetic findings from combinatorial analyses of severe acute COVID-19 patients and an ME/CFS population and we expect that access to additional independent, larger patient datasets will further improve the disease insights and validate potential treatment options in long COVID.

show abstract

Systematic indication extension for drugs using patient stratification insights generated by combinatorial analytics

Cited by 6 publications

References 53 publications

Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis

Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis

Prospecção Tecnológica sobre o Setor de Plataformas de Inteligência Artificial Aplicadas ao Reposicionamento de Drogas Contra a COVID-19

Genetic Risk Factors for Severe and Fatigue Dominant Long COVID and Commonalities with ME/CFS Identified by Combinatorial Analysis

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