cando.py: Open Source Software for Predictive Bioanalytics of Large Scale Drug–Protein–Disease Data

Mangione, William; Falls, Zackary; Chopra, Gaurav; Samudrala, Ram

doi:10.1021/acs.jcim.0c00110

Cited by 26 publications

(35 citation statements)

References 40 publications

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“…This method has been applied successfully to analyze repurposing of ribavririn and a novel compound LMK-235 in breast cancer and AML. The results have been validated in in vivo experiments and are being considered to enter a clinical phase in the near future ( 182 ). These are but a handful of examples that, however, make us anticipate further, near-future developments, in the high-throughput study of phenomena of interest for systematic drug repositioning strategies to treat cancer.…”

Section: Discussionmentioning

confidence: 93%

Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics

Hernández‐Lemus

Martínez-García

2021

Front. Oncol.

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Cancer is a set of complex pathologies that has been recognized as a major public health problem worldwide for decades. A myriad of therapeutic strategies is indeed available. However, the wide variability in tumor physiology, response to therapy, added to multi-drug resistance poses enormous challenges in clinical oncology. The last years have witnessed a fast-paced development of novel experimental and translational approaches to therapeutics, that supplemented with computational and theoretical advances are opening promising avenues to cope with cancer defiances. At the core of these advances, there is a strong conceptual shift from gene-centric emphasis on driver mutations in specific oncogenes and tumor suppressors—let us call that the silver bullet approach to cancer therapeutics—to a systemic, semi-mechanistic approach based on pathway perturbations and global molecular and physiological regulatory patterns—we will call this the shrapnel approach. The silver bullet approach is still the best one to follow when clonal mutations in driver genes are present in the patient, and when there are targeted therapies to tackle those. Unfortunately, due to the heterogeneous nature of tumors this is not the common case. The wide molecular variability in the mutational level often is reduced to a much smaller set of pathway-based dysfunctions as evidenced by the well-known hallmarks of cancer. In such cases “shrapnel gunshots” may become more effective than “silver bullets”. Here, we will briefly present both approaches and will abound on the discussion on the state of the art of pathway-based therapeutic designs from a translational bioinformatics and computational oncology perspective. Further development of these approaches depends on building collaborative, multidisciplinary teams to resort to the expertise of clinical oncologists, oncological surgeons, and molecular oncologists, but also of cancer cell biologists and pharmacologists, as well as bioinformaticians, computational biologists and data scientists. These teams will be capable of engaging on a cycle of analyzing high-throughput experiments, mining databases, researching on clinical data, validating the findings, and improving clinical outcomes for the benefits of the oncological patients.

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Section: Discussionmentioning

confidence: 93%

Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics

Hernández‐Lemus

Martínez-García

2021

Front. Oncol.

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“…Drug repurposing is the practice of finding new uses for existing drugs, taking advantage of prior safety, efficacy, and pharmacological knowledge and data [15]. Drug repurposing has the potential to arbitrarily increase the utility of the FDA approved drug library [17,20,21], particularly via innovations such as multitarget drug repurposing [22,23]. Drug repurposing has yielded new uses for multiple drugs [15,17] and has demonstrated potential for the treatment of viral [24,25], bacterial [26], and complex indications such as cancer [17].…”

Section: Drug Repurposingmentioning

confidence: 99%

Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform

Hudson¹,

Samudrala²

2021

Preprint

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Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multidisease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines via large scale modelling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is then compared to all other signatures that are then sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions in the platform used to create the drug-proteome signatures may be determined by any screening or docking method but the primary approach used thus far has been an in house similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and cheminformatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the corresponding two docking-based pipelines it was synthesized from, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking based signature generation methods can capture unique and useful signal for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.

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“…Drug repurposing is the practice of finding new uses for existing drugs, taking advantage of prior safety, efficacy, and pharmacological knowledge and data [ 15 ]. Drug repurposing has the potential to arbitrarily increase the utility of the FDA-approved drug library [ 17 , 20 , 21 ], particularly via innovations such as multitarget drug repurposing [ 22 , 23 ]. Drug repurposing has yielded new uses for multiple drugs [ 15 , 17 ] and has demonstrated potential for the treatment of viral [ 24 , 25 ], bacterial [ 26 ], and complex indications such as cancer [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform

Hudson

Samudrala

2021

Molecules

Self Cite

View full text Add to dashboard Cite

Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.

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cando.py: Open Source Software for Predictive Bioanalytics of Large Scale Drug–Protein–Disease Data

Cited by 26 publications

References 40 publications

Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics

Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics

Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform

Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform

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