Systematic review of computational methods for drug combination prediction

Synergistic effects between drugs are rare and highly context-dependent and patient-specific. Hence, there is a need to develop novel approaches to stratify patients for optimal therapy regimens, especially in the context of personalized design of combinatorial treatments. Computational methods enable systematic in-silico screening of combination effects, and can thereby prioritize most potent combinations for further testing, among the massive number of potential combinations. To help researchers to choose a prediction method that best fits for various real-world applications, we carried out a systematic literature review of 117 computational methods developed to date for drug combination prediction, and classified the methods in terms of their combination prediction tasks and input data requirements. Most current methods focus on prediction or classification of combination synergy, and only a few methods consider the efficacy and potential toxicity of the combinations, which are the key determinants of therapeutic success of drug treatments. Furthermore, there is a need to further develop methods that enable dose-specific predictions of combination effects across multiple doses, which is important for clinical translation of the predictions, as well as model-based identification of biomarkers predictive of heterogeneous drug combination responses. Even if most of the computational methods reviewed focus on anticancer applications, many of the modelling approaches are also applicable to antiviral and other diseases or indications.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.Peer reviewe

Chemogenomic library design strategies for precision oncology, applied to phenotypic profiling of glioblastoma patient cells

Summary: Designing a targeted screening library of bioactive small molecules is a challenging task since most compounds modulate their effects through multiple protein targets with varying degrees of potency and selectivity. We implemented analytic procedures for designing anticancer compound libraries adjusted for library size, cellular activity, chemical diversity and availability, and target selectivity. The resulting compound collections cover a wide range of protein targets and biological pathways implicated in various cancers, making them widely applicable to precision oncology. We characterized the compound and target spaces of the virtual libraries, in comparison with a minimal screening library of 1,211 compounds for targeting 1,386 anticancer proteins. In a pilot screening study, we identified patient-specific vulnerabilities by imaging glioma stem cells from patients with glioblastoma (GBM), using a physical library of 789 compounds that cover 1,320 of the anticancer targets. The cell survival profiling revealed highly heterogeneous phenotypic responses across the patients and GBM subtypes

Discovery of host-directed modulators of virus infection by probing the SARS-CoV-2-host protein-protein interaction network

The ongoing coronavirus disease 2019 (COVID-19) pandemic has highlighted the need to better understand virus-host interactions. We developed a network-based method that expands the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-host protein interaction network and identifies host targets that modulate viral infection. To disrupt the SARS-CoV-2 interactome, we systematically probed for potent compounds that selectively target the identified host proteins with high expression in cells relevant to COVID-19. We experimentally tested seven chemical inhibitors of the identified host proteins for modulation of SARS-CoV-2 infection in human cells that express ACE2 and TMPRSS2. Inhibition of the epigenetic regulators bromodomain-containing protein 4 (BRD4) and histone deacetylase 2 (HDAC2), along with ubiquitin-specific peptidase (USP10), enhanced SARS-CoV-2 infection. Such proviral effect was observed upon treatment with compounds JQ1, vorinostat, romidepsin and spautin-1, when measured by cytopathic effect and validated by viral RNA assays, suggesting that the host proteins HDAC2, BRD4 and USP10 have antiviral functions. We observed marked differences in antiviral effects across cell lines, which may have consequences for identification of selective modulators of viral infection or potential antiviral therapeutics. While network-based approaches enable systematic identification of host targets and selective compounds that may modulate the SARS-CoV-2 interactome, further developments are warranted to increase their accuracy and cell-context specificity.Peer reviewe

Predicting Target Profile using Cross Venn-ABERS Predictors

In this thesis, Cross Venn-ABERS Predictors (CVAPs) were used as aConformal Prediction technique on an Support Vector Machines (SVM)Algorithm as the underlying algorithm. The datasets, one for each target,were extracted from ChEMBL 24.1 database. After balancing the datasetsin order to get a proper format to be used as training sets, cross-validationwas followed in order to investigate whether there is a statistical significancebetween the LibLINEAR and LibSVM methods. The impact of removingduplicated set of molecular descriptors was also investigated. After decidingthat LibSVM with duplicates excluded performs best, models were trainedand used in a kinase inhibitors dataset in order to get a profile for each target.A Principal Component Analysis (PCA) was performed on those results.The variance explained was relatively low and there was no clusteringbetween the observations

Predicting Target Profile using Cross Venn-ABERS Predictors

In this thesis, Cross Venn-ABERS Predictors (CVAPs) were used as aConformal Prediction technique on an Support Vector Machines (SVM)Algorithm as the underlying algorithm. The datasets, one for each target,were extracted from ChEMBL 24.1 database. After balancing the datasetsin order to get a proper format to be used as training sets, cross-validationwas followed in order to investigate whether there is a statistical significancebetween the LibLINEAR and LibSVM methods. The impact of removingduplicated set of molecular descriptors was also investigated. After decidingthat LibSVM with duplicates excluded performs best, models were trainedand used in a kinase inhibitors dataset in order to get a profile for each target.A Principal Component Analysis (PCA) was performed on those results.The variance explained was relatively low and there was no clusteringbetween the observations

Computational Pipeline for Rational Drug Combination Screening in Patient-Derived Cells

Funding Information: The authors thank the High-Throughput Chemical Biology Screening Platform at the Centre for Molecular Medicine Norway (NCMM) for assistance with the combination screening experiments. The work was supported by the grants from Helse Sør-Øst (2020026 to TA), the Norwegian Cancer Society (216104 to TA), the Radium Hospital Foundation (TA), the Academy of Finland (310507, 313267, 326238, and 344698 to TA), the Sigrid Jusélius Foundation (TA), the Finnish Cancer Foundation (TA), and the ERANET PerMed Co-Fund (projects JAKSTAT-TARGET to TA and CLL-CLUE to TA and SS). Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.In many complex diseases, such as cancers, resistance to monotherapies easily occurs, and longer-term treatment responses often require combinatorial therapies as next-line regimens. However, due to a massive number of possible drug combinations to test, there is a need for systematic and rational approaches to finding safe and effective drug combinations for each individual patient. This protocol describes an ecosystem of computational methods to guide high-throughput combinatorial screening that help experimental researchers to identify optimal drug combinations in terms of synergy, efficacy, and/or selectivity for further preclinical and clinical investigation. The methods are demonstrated in the context of combinatorial screening in primary cells of leukemia patients, where the translational aim is to identify drug combinations that show not only high synergy but also maximal cancer-selectivity. The mechanism-agnostic and cost-effective computational methods are widely applicable to various cancer types, which are amenable to drug testing, as the computational methods take as input only the phenotypic measurements of a subset of drug combinations, without requiring target information or genomic profiles of the patient samples.Peer reviewe