Abstract: Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.

Abstract: This analysis presents a systematic evaluation of the extent of therapeutic opportunities that can be obtained from drug repurposing by connecting drug targets with disease genes. When using FDA-approved indications as a reference level we found that drug repurposing can offer an average of an 11-fold increase in disease coverage, with the maximum number of diseases covered per drug being increased from 134 to 167 after extending the drug targets with their high confidence first neighbors. Additionally, by network analysis to connect drugs to disease modules we found that drugs on average target 4 disease modules, yet the similarity between disease modules targeted by the same drug is generally low and the maximum number of disease modules targeted per drug increases from 158 to 229 when drug targets are neighbor-extended. Moreover, our results highlight that drug repurposing is more dependent on target proteins being shared between diseases than on polypharmacological properties of drugs. We apply our drug repurposing and network module analysis to COVID-19 and show that Fostamatinib is the drug with the highest module coverage.

Abstract: Analysis of nucleotide variants is a cornerstone of cancer medicine. Although only 2% of the genomic sequence is protein coding, mutations occurring in these regions have the potential to influence protein structure or modification status and may have severe impact on disease aetiology. Proteogenomics enables the analysis of sample-specific nonsynonymous nucleotide variants with regard to their effect at the proteome and phosphoproteome levels. Here, we developed a proof-of-concept proteogenomics workflow and applied it to the malignant melanoma cell line A375. Initially, we studied the resistance to serine/threonine-protein kinase B-raf (BRAF) inhibitor (BRAFi) vemurafenib in A375 cells. This allowed identification of several oncogenic nonsynonymous nucleotide variants, including a gain-of-function variant on aurora kinase A (AURKA) at F31I. We also detected significant changes in abundance among (phospho)proteins, which led to reactivation of the MAPK signaling pathway in BRAFi-resistant A375 cells. Upon reconstruction of the multiomic integrated signaling networks, we predicted drug therapies with the potential to disrupt BRAFi resistance mechanism in A375 cells. Notably, we showed that AURKA inhibition is effective and specific against BRAFi-resistant A375 cells. Subsequently, we investigated amino acid variants that interfere with protein posttranslational modification (PTM) status and potentially influence A375 cell signaling irrespective of BRAFi resistance. Mass spectrometry (MS) measurements confirmed variant-driven PTM changes in 12 proteins. Among them was the runt-related transcription factor 1 (RUNX1) displaying a variant on a known phosphorylation site S(Ph)276L. We confirmed the loss of phosphorylation site by MS and demonstrated the impact of this variant on RUNX1 interactome.

Abstract: The global epidemic caused by the coronavirus severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has resulted in the infection of over 200 million people. To extend the knowledge of interactions between SARS-CoV-2 and humans, we systematically investigate the interactome of 29 viral proteins in human cells by using an antibody-based TurboID assay. In total, 1,388 high-confidence human proximal proteins with biotinylated sites are identified. Notably, we find that SARS-CoV-2 manipulates the antiviral and immune responses. We validate that the membrane protein ITGB1 associates angiotensin-converting enzyme 2 (ACE2) to mediate SARS-CoV-2 entry. Moreover, we reveal that SARS-CoV-2 proteins inhibit activation of the interferon pathway through the mitochondrial protein mitochondrial antiviral-signaling protein (MAVS) and the methyltransferase SET domain containing 2, histone lysine methyltransferase (SETD2). We propose 111 potential drugs for the clinical treatment of coronavirus disease 2019 (COVID-19) and identify three compounds that significantly inhibit the replication of SARS-CoV-2. The proximity labeling map of SARS-CoV-2 and humans provides a resource for elucidating the mechanisms of viral infection and developing drugs for COVID-19 treatment.

Abstract: 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 approximately 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 DeltaDeltaG 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.

Abstract: Cardiovascular diseases (CDs) are a major concern in the human race and one of the leading causes of death worldwide. beta-Adrenergic receptors (beta1-AR and beta2-AR) play a crucial role in the overall regulation of cardiac function. In the present study, structure-based virtual screening, machine learning (ML), and a ligand-based similarity search were conducted for the PubChem database against both beta1- and beta2-AR. Initially, all docked molecules were screened using the threshold binding energy value. Molecules with a better binding affinity were further used for segregation as active and inactive through ML. The pharmacokinetic assessment was carried out on molecules retained in the above step. Further, similarity searching of the ChEMBL and DrugBank databases was performed. From detailed analysis of the above data, four compounds for each of beta1- and beta2-AR were found to be promising in nature. A number of critical ligand-binding amino acids formed potential hydrogen bonds and hydrophobic interactions. Finally, a molecular dynamics (MD) simulation study of each molecule bound with the respective target was performed. A number of parameters obtained from the MD simulation trajectories were calculated and substantiated the stability between the protein-ligand complex. Hence, it can be postulated that the final molecules might be crucial for CDs subjected to experimental validation.

Abstract: In the COVID-19 pandemic, drug repositioning has presented itself as an alternative to the time-consuming process of generating new drugs. This review describes a drug repurposing process that is based on a new data-driven approach: we put forward five information paths that associate COVID-19-related genes and COVID-19 symptoms with drugs that directly target these gene products, that target the symptoms or that treat diseases that are symptomatically or genetically similar to COVID-19. The intersection of the five information paths results in a list of 13 drugs that we suggest as potential candidates against COVID-19. In addition, we have found information in published studies and in clinical trials that support the therapeutic potential of the drugs in our final list.

Abstract: PURPOSE: This review provides an update on the current clinical, epidemiological and pathophysiological evidence alongside the diagnostic, prevention and treatment approach to chemotherapy-induced peripheral neuropathy (CIPN). FINDINGS: The incidence of cancer and long-term survival after treatment is increasing. CIPN affects sensory, motor and autonomic nerves and is one of the most common adverse events caused by chemotherapeutic agents, which in severe cases leads to dose reduction or treatment cessation, with increased mortality. The primary classes of chemotherapeutic agents associated with CIPN are platinum-based drugs, taxanes, vinca alkaloids, bortezomib and thalidomide. Platinum agents are the most neurotoxic, with oxaliplatin causing the highest prevalence of CIPN. CIPN can progress from acute to chronic, may deteriorate even after treatment cessation (a phenomenon known as coasting) or only partially attenuate. Different chemotherapeutic agents share both similarities and key differences in pathophysiology and clinical presentation. The diagnosis of CIPN relies heavily on identifying symptoms, with limited objective diagnostic approaches targeting the class of affected nerve fibres. Studies have consistently failed to identify at-risk cohorts, and there are no proven strategies or interventions to prevent or limit the development of CIPN. Furthermore, multiple treatments developed to relieve symptoms and to modify the underlying disease in CIPN have failed. IMPLICATIONS: The increasing prevalence of CIPN demands an objective approach to identify at-risk patients in order to prevent or limit progression and effectively alleviate the symptoms associated with CIPN. An evidence base for novel targets and both pharmacological and non-pharmacological treatments is beginning to emerge and has been recognised recently in publications by the American Society of Clinical Oncology and analgesic trial design expert groups such as ACTTION.

Abstract: Our understanding of metastatic prostate cancer (mPrCa) has dramatically advanced during the genomics era. Nonetheless, many aspects of the disease may still be uncovered through reanalysis of public datasets. We integrated the expression datasets for 209 PrCa tissues (metastasis, primary, normal) with expression, gene dependency (GD) (from CRISPR/cas9 screen), and drug viability data for hundreds of cancer lines (including PrCa). Comparative statistical and pathways analyses and functional annotations (available inhibitors, protein localization) revealed relevant pathways and potential (and previously reported) protein markers for minimally invasive mPrCa diagnostics. The transition from localized to mPrCa involved the upregulation of DNA replication, mitosis, and PLK1-mediated events. Genes highly upregulated in mPrCa and with very high average GD (~1) are potential therapeutic targets. We showed that fostamatinib (which can target PLK1 and other over-expressed serine/threonine kinases such as AURKA, MELK, NEK2, and TTK) is more active against cancer lines with more pronounced signatures of invasion (e.g., extracellular matrix organization/degradation). Furthermore, we identified surface-bound (e.g., ADAM15, CD276, ABCC5, CD36, NRP1, SCARB1) and likely secreted proteins (e.g., APLN, ANGPT2, CTHRC1, ADAM12) that are potential mPrCa diagnostic markers. Overall, we demonstrated that comprehensive analyses of public genomics data could reveal potentially clinically relevant information regarding mPrCa.

Abstract: BACKGROUND: The study of drug-target interactions (DTIs) affinity plays an important role in safety assessment and pharmacology. Currently, quantitative structure-activity relationship (QSAR) and molecular docking (MD) are most common methods in research of DTIs affinity. However, they often built for a specific target or several targets, and most QSAR and MD methods were based either on structure of drug molecules or on structure of receptors with low accuracy and small scope of application. How to construct quantitative prediction models with high accuracy and wide applicability remains a challenge. To this end, this paper screened molecular descriptors based on molecular vibrations and took molecule-target as a whole system to construct prediction models with high accuracy-wide applicability based on dissociation constant (Kd) and concentration for 50% of maximal effect (EC50), and to provide reference for quantifying affinity of DTIs. RESULTS: After comprehensive comparison, the results showed that RF models are optimal models to analyze and predict DTIs affinity with coefficients of determination (R(2)) are all greater than 0.94. Compared to the quantitative models reported in literatures, the RF models developed in this paper have higher accuracy and wide applicability. In addition, E-state molecular descriptors associated with molecular vibrations and normalized Moreau-Broto autocorrelation (G3), Moran autocorrelation (G4), transition-distribution (G7) protein descriptors are of higher importance in the quantification of DTIs. CONCLUSION: Through screening molecular descriptors based on molecular vibrations and taking molecule-target as whole system, we obtained optimal models based on RF with more accurate-widely applicable, which indicated that selection of molecular descriptors associated with molecular vibrations and the use of molecular-target as whole system are reliable methods for improving performance of models. It can provide reference for quantifying affinity of DTIs.