Potential Drug Combinations Research Articles

Combination of Hydroxychloroquine, Melatonin and Mercaptopurine as a Possible Intervention for Prophylaxis and Treatment of Novel COVID-19 Infection

Background: COVID-19 has been declared as a pandemic recently and has caused many deaths worldwide. Till date, no effective drug or vaccine is available against SARS-CoV-2. There is an urgent need to find effective alternative preventive and treatment strategies to deal with the SARS-CoV- 2 outbreak. Objective: This communication proposes a new potential drug combination (repurposed) for prophylaxis and treatment of SARS-CoV-2. Methods and Materials: We performed a brief review of literature on a combination of Hydroxychloroquine, Melatonin and Mercaptopurine for prophylaxis and treatment of Novel COVID-19 infection and also assessed their possible mechanism of action against SARS-CoV-2. Observation: Proposed combination seems to be safe and target is unlikely to develop resistance to this combination. Conclusion: This scientific review proposes potential repurposed drugs and their combination targeting SARS-CoV-2. Conclusion: This scientific review proposes potential candidate repurposed drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.

GW4064 enhances the chemosensitivity of colorectal cancer to oxaliplatin by inducing pyroptosis

Repeated and long-term oxaliplatin therapy leads to drug resistance and severe adverse events, which limit its clinical use. These difficulties highlight the importance of identifying potent and specific drug combinations to enhance the antitumor effects of oxaliplatin. The farnesoid X receptor (FXR) deficiency in colorectal cancer (CRC) suggests that restoring FXR function might be a promising strategy for CRC treatment. A drug combination study showed that the GW4064 acted synergistically with oxaliplatin in colon cancer cells. The combination of oxaliplatin plus GW4064 inhibited cell growth and colony formation, induced apoptosis and pyroptosis in vitro, and slowed tumor growth in vivo. Mechanistically, GW4064 enhanced the chemosensitivity of cells to oxaliplatin by inducing BAX/caspase-3/GSDME-mediated pyroptosis. Furthermore, the combination of oxaliplatin and GW4064 synergistically inhibited STAT3 signaling by restoring SHP expression. Our study revealed that GW4064 could enhance the antitumor effects of oxaliplatin against CRC, which provides a novel therapeutic strategy based on a combinational approach for CRC treatment.

An In Silico Method for Predicting Drug Synergy Based on Multitask Learning.

To make better use of all kinds of knowledge to predict drug synergy, it is crucial to successfully establish a drug synergy prediction model and leverage the reconstruction of sparse known drug targets. Therefore, we present an in silico method that predicts the synergy scores of drug pairs based on multitask learning (DSML) that could fuse drug targets, protein-protein interactions, anatomical therapeutic chemical codes, a priori knowledge of drug combinations. To simultaneously reconstruct drug-target protein interactions and synergistic drug combinations, DSML benefits indirectly from the associations with relation through proteins. In cross-validation experiments, DSML improved the ability to predict drug synergy. Moreover, the reconstruction of drug-target interactions and the incorporation of multisource knowledge significantly improved drug combination predictions by a large margin. The potential drug combinations predicted by DSML demonstrate its ability to predict drug synergy.

Identification of drug combinations on the basis of machine learning to maximize anti-aging effects.

Aging is a multifactorial process that involves numerous genetic changes, so identifying anti-aging agents is quite challenging. Age-associated genetic factors must be better understood to search appropriately for anti-aging agents. We utilized an aging-related gene expression pattern-trained machine learning system that can implement reversible changes in aging by linking combinatory drugs. In silico gene expression pattern-based drug repositioning strategies, such as connectivity map, have been developed as a method for unique drug discovery. However, these strategies have limitations such as lists that differ for input and drug-inducing genes or constraints to compare experimental cell lines to target diseases. To address this issue and improve the prediction success rate, we modified the original version of expression profiles with a stepwise-filtered method. We utilized a machine learning system called deep-neural network (DNN). Here we report that combinational drug pairs using differential expressed genes (DEG) had a more enhanced anti-aging effect compared with single independent treatments on leukemia cells. This study shows potential drug combinations to retard the effects of aging with higher efficacy using innovative machine learning techniques.

Network analysis and molecular mapping for SARS-CoV-2 to reveal drug targets and repurposing of clinically developed drugs

Novel coronavirus (SARS–CoV–2), turned out to be a global pandemic with unstoppable morbidity and mortality rate. However, till date there is no effective treatment found against SARS–CoV–2. We report on the major in–depth molecular and docking analysis by using antiretroviral (Lopinavir and ritonavir), antimalarial (Hydroxychloroquine), antibiotics (Azithromycin), and dietary supplements (Vitamin C and E) to provide new insight into drug repurposing molecular events involved in SARS–CoV–2. We constructed three drug–target–pathways–disease networks to predict the targets and drugs interactions as well as important pathways involved in SARS–CoV–2. The results suggested that by using the combination of Lopinavir, Ritonavir along with Hydroxychloroquine and Vitamin C may turned out to be the effective line of treatment for SARS–CoV–2 as it shows the involvement of PARP–1, MAPK–8, EGFR, PRKCB, PTGS–2, and BCL–2. Gene ontology biological process analysis further confirmed multiple viral infection–related processes (P < 0.001), including viral life cycle, modulation by virus, C–C chemokine receptor activity, and platelet activation. KEGG pathway analysis involves multiple pathways (P < 0.05), including FoxO, GnRH, ErbB, Neurotrophin, Toll–like receptor, IL–17, TNF, Insulin, HIF–1, JAK–STAT, Estrogen, NF–kappa, Chemokine, VEGF, and Thyroid hormone signaling pathway in SARS–CoV–2. Docking study was carried out to predict the molecular mechanism Thus, the potential drug combinations could reduce viral infectivity, viral replication, and abnormal host inflammatory responses and may be useful for multi–target drugs against SARS–CoV–2.

Multiobjective optimization identifies cancer-selective combination therapies

Combinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient, using cost-effective methods. Here, we developed an exact multiobjective optimization method for identifying pairwise or higher-order combinations that show maximal cancer-selectivity. The prioritization of patient-specific combinations is based on Pareto-optimization in the search space spanned by the therapeutic and nonselective effects of combinations. We demonstrate the performance of the method in the context of BRAF-V600E melanoma treatment, where the optimal solutions predicted a number of co-inhibition partners for vemurafenib, a selective BRAF-V600E inhibitor, approved for advanced melanoma. We experimentally validated many of the predictions in BRAF-V600E melanoma cell line, and the results suggest that one can improve selective inhibition of BRAF-V600E melanoma cells by combinatorial targeting of MAPK/ERK and other compensatory pathways using pairwise and third-order drug combinations. Our mechanism-agnostic optimization method is widely applicable to various cancer types, and it takes as input only measurements of a subset of pairwise drug combinations, without requiring target information or genomic profiles. Such data-driven approaches may become useful for functional precision oncology applications that go beyond the cancer genetic dependency paradigm to optimize cancer-selective combinatorial treatments.

The perspectives of interleukin-10 in the pathogenesis and therapeutics of multiple myeloma.

Multiple myeloma (MM) is typically featured by the increased levels of inflammatory cytokines in the neoplastic plasma cells (PCs) producing monoclonal immunoglobulin. PCs proliferate in the bone marrow, which will lead to extensive skeletal destruction with osteolytic lesions, osteopenia, or pathologic fractures. The diagnostic biology of MM has progressed from morphology and low-sensitivity protein analysis into multiomics-based high-throughput readout, whereas therapeutics has evolved from single active agent to potential active drug combinations underlying precision medicine. Many studies have focused on the cytokine networks that control growth, progression, and dissemination of the disease. The complexity of cytokines in MM development remains to be elucidated comprehensively. Apart from knowing that interleukin (IL)-6 is important in the pathogenesis of MM, it has been shown that IL-6 is a paracrine factor supplied by the microenvironment comprising of those cells from the myeloid compartment. Due to IL-10 was considered an immunosuppressive cytokine to promote cancer escape from immune surveillance, the role of IL-10 in this regard has been underestimated although recent advances have reported that IL-10 induces both PC proliferation and angiogenesis in MM. In addition, cumulative studies have suggested that IL-10 plays an important role in the induction of chemoresistance in many cancers; a virtual requirement of autocrine IL-10 for MM cells to escape from an IL-6-dependent proliferation loop was implicated. In this review, we summarize the available information to elucidate a new understanding of the molecular and functional roles of IL-10 in MM.

Pyrotinib enhances the radiosensitivity of HER2‑overexpressing gastric and breast cancer cells.

The overexpression or amplification of HER2 has been observed in a significant proportion of both gastric cancer (GC) and breast cancer (BC) cases. Pyrotinib is an irreversible dual (EGFR/HER2) tyrosine kinase inhibitor (TKI), newly evaluated for the treatment of HER2-overexpressing cancer types. As radiotherapy (RT) serves a crucial role in controlling the local recurrence of GC and BC, the present study investigated the impact of pyrotinib on the irradiation response. The current results demonstrated that pyrotinib enhanced the radiosensitivity of HER2-overexpressing GC and BC cells in vitro and in vivo. In both NCI-N87 and SKBR3 cells, pyrotinib suppressed the irradiation-induced HER2 nuclear transport. Furthermore, pyrotinib increased DNA damage induced by irradiation in both cancer cell lines. Pyrotinib also enhanced the cytotoxicity of docetaxel, which may provide a novel strategy for potential drug combinations. Thus, pyrotinib is a promising irradiation sensitizer in patients with HER2-overexpressing GC and BC. The present results provide a theoretical foundation for further clinical evaluation of pyrotinib.

MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs.

Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or 2019-nCoV), there is an urgent need to identify therapeutics that are effective against COVID-19 before vaccines are available. Since the current rate of SARS-CoV-2 knowledge acquisition via traditional research methods is not sufficient to match the rapid spread of the virus, novel strategies of drug discovery for SARS-CoV-2 infection are required. Structure-based virtual screening for example relies primarily on docking scores and does not take the importance of key residues into consideration, which may lead to a significantly higher incidence rate of false-positive results. Our novel in silico approach, which overcomes these limitations, can be utilized to quickly evaluate FDA-approved drugs for repurposing and combination, as well as designing new chemical agents with therapeutic potential for COVID-19. As a result, anti-HIV or antiviral drugs (lopinavir, tenofovir disoproxil, fosamprenavir and ganciclovir), antiflu drugs (peramivir and zanamivir) and an anti-HCV drug (sofosbuvir) are predicted to bind to 3CLPro in SARS-CoV-2 with therapeutic potential for COVID-19 infection by our new protocol. In addition, we also propose three antidiabetic drugs (acarbose, glyburide and tolazamide) for the potential treatment of COVID-19. Finally, we apply our new virus chemogenomics knowledgebase platform with the integrated machine-learning computing algorithms to identify the potential drug combinations (e.g. remdesivir+chloroquine), which are congruent with ongoing clinical trials. In addition, another 10 compounds from CAS COVID-19 antiviral candidate compounds dataset are also suggested by Molecular Complex Characterizing System with potential treatment for COVID-19. Our work provides a novel strategy for the repurposing and combinations of drugs in the market and for prediction of chemical candidates with anti-COVID-19 potential.

Simultaneous Determination of Telmisartan and Pitavastatin Calcium in Intestinal Perfusate by HPLC: Application to Intestinal Absorption Interaction Study.

Hypertension and hypercholesterolemia are two main physiological risk factors of cardiovascular disease, and commonly occur in combination. Multicompound combination therapy is rational for the treatment of concurrent hypertension and hypercholesterolemia, while telmisartan and pitavastatin calcium can be used as a potential drug combination. The aim of this paper is to study the intestinal absorption and absorption interaction of telmisartan and pitavastatin calcium. An HPLC method was developed and validated to determine telmisartan and pitavastatin calcium in intestinal perfusate simultaneously. The in situ single-pass perfusion in rats was utilized to investigate the effects of concentrations, intestinal segment (duodenum, jejunum, ileum and colon) and co-administrated drugs on absorption. The effective permeability coefficient and the absorption rate constant of telmisartan were higher in the duodenum as compared to other intestinal segments. However, the intestinal absorption of pitavastatin calcium was not segmental dependent. The effective permeability coefficient and absorption rate constant have no significant difference among three concentrations of telmisartan, pitavastatin calcium individually and their combination. The results showed that telmisartan and pitavastatin calcium were transported passively, and telmisartan and pitavastatin calcium could be absorbed well in all intestinal segments. The intestinal absorption parameters revealed the absence of any intestinal absorption interaction when co-administered. Lay Summary: Co-administration of telmisartan and pitavastatin calcium can provide a potential therapeutic strategy for the treatment of concurrent hypertension and hypercholesterolemia. We are investigating the intestinal interaction of these two drugs in rats using the developed HPLC method and in situ single-pass perfusion technology. We will calculate some parameters after administrating two types of drugs either separately or together, which help reflect changes regarding intestinal absorption and penetration. Compared with telmisartan and pitavastatin calcium administrated separately, if parameters significantly change after co-administration, it proves the existence of the intestinal interactions. Moreover, the results might contribute to clinic drug monitoring.

Optimized low-dose combinatorial drug treatment boosts selectivity and efficacy of colorectal carcinoma treatment.

The current standard of care for colorectal cancer (CRC) is a combination of chemotherapeutics, often supplemented with targeted biological drugs. An urgent need exists for improved drug efficacy and minimized side effects, especially at late-stage disease. We employed the phenotypically driven therapeutically guided multidrug optimization (TGMO) technology to identify optimized drug combinations (ODCs) in CRC. We identified low-dose synergistic and selective ODCs for a panel of six human CRC cell lines also active in heterotypic 3D co-culture models. Transcriptome sequencing and phosphoproteome analyses showed that the mechanisms of action of these ODCs converged toward MAP kinase signaling and cell cycle inhibition. Two cell-specific ODCs were translated to in vivo mouse models. The ODCs reduced tumor growth by ~80%, outperforming standard chemotherapy (FOLFOX). No toxicity was observed for the ODCs, while significant side effects were induced in the group treated with FOLFOX therapy. Identified ODCs demonstrated significantly enhanced bioavailability of the individual components. Finally, ODCs were also active in primary cells from CRC patient tumor tissues. Taken together, we show that the TGMO technology efficiently identifies selective and potent low-dose drug combinations, optimized regardless of tumor mutation status, outperforming conventional chemotherapy.

Identifying Drug Sensitivity Subnetworks with NETPHIX

SummaryPhenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. We develop a computational method, named NETPHIX (NETwork-to-PHenotype association with eXclusivity) to identify subnetworks of genes whose genetic alterations are associated with drug response or other continuous cancer phenotypes. Leveraging interaction information among genes and properties of cancer mutations such as mutual exclusivity, we formulate the problem as an integer linear program and solve it optimally to obtain a subnetwork of associated genes. Applied to a large-scale drug screening dataset, NETPHIX uncovered gene modules significantly associated with drug responses. Utilizing interaction information, NETPHIX modules are functionally coherent and can thus provide important insights into drug action. In addition, we show that modules identified by NETPHIX together with their association patterns can be leveraged to suggest drug combinations.

Qingchang Huashi Formula attenuates DSS-induced colitis in mice by restoring gut microbiota-metabolism homeostasis and goblet cell function

Ethnopharmacological relevanceInflammatory bowel disease (IBD) is a chronic and relapsing inflammatory disease of the gastrointestinal tract, consisting of ulcerative colitis (UC) and Crohn's disease (CD). Gut microbiota and their metabolites may play a role in the pathogen of IBD, especially of the UC. Qingchang Huashi Formula (QHF), a traditional Chinese medicine formula, has shown therapeutic effect on treating UC based on the clinical practice without clear pharmacological mechanism. Aim of the studyThe aim of this study was to clearly define the effect of QHF and its components, Baitouweng (PBR) and Baizhi (ADR) on treating UC. Materials and MethodsPharmacodynamic effects of QHF and single herb were evaluated in dextran sulfate sodium (DSS) induced acute or chronic colitis mice. Body weight loss, disease activity index (DAI) and colon length were estimated. Histological changes were observed by H&E staining. The number and abundance of gut microbiota were measured with 16S rRNA sequencing. LC-MS and GC-MS were used to detect the concentration of metabolites (e.g., bile acids (BAs) and short chain fatty acids (SCFAs)). The goblet cell was observed by Alcian blue/periodic acid-Schiff (AB/PAS) straining and the crypt stem cell was estimated by immunohistochemical analyses. The colorectal tissues were used to detect levels of IL-1β, IL-6 and TNF-α by ELISA or qRT-PCR. The expression of NLRP3, Caspase 1 and IL-1β were examined by western blotting. ResultsQHF significantly inhibited colitis, protected mice from the loss of body weight and colon shorten. Comparatively, ADR and PBR showed strong efficacy in inhibiting DSS-induced colitis. We verified that while ADR was responsible for QHF's effect on maintaining gut microbiota homeostasis and metabolism, PBR was more prominent in keeping crypt stem cells proliferation and colonic goblet cells function. Moreover, we demonstrated that the alleviation of colitis by QHF was associated with the restoration of gut microbiota-metabolism homeostasis, protection of intestinal epithelial barrier and regulation of NLRP3/IL-1β pathway. ConclusionsThe finding of the present study suggested that QHF is curative in DSS-induced colitis by restoring gut microbiota-metabolism homeostasis and goblet cells function. An optimized QHF was constituted by ADR and PBR, which showed comparable efficacy on colitis to that of QHF. Our work probed out the active constitutes as well as the relevant pharmacological mechanisms of QHF, shedding light on potential new drug combination for the treatment of IBD.

A medical network clustering method with weighted graph structure

Today, most of the databases used for drug information mining are derived from the collection of many treatments under a single disease, and some special drug compatibility rules can be found from them. However, researchers’ exploration of medical data is not limited to this. The comparative analysis of drugs for different diseases has become a new research point. In this paper, the drug is used as a node, the relationship is the edge connecting the two nodes, the co-occurrence frequency of the drug is used as the weight of the edge to establish a network graph. We use the clustering algorithm of the weighted network graph center diffusion method combining the network topology and the edge weights to divide the network graph into communities. Then we proposed the Structural Clustering Algorithm on Weighted Networks (SCW), it helps to study the prescription of medical prescriptions and provides more scientific recommendations for auxiliary prescriptions. In the experiment, SCW is compared with the classic community discovery algorithm CPM, the network function modular analysis algorithm MCODE and the hierarchical network graph structure analysis algorithm BGLL. We analyze the results according to NMI, ARI and F-Measure. Finally, a case study of real data was conducted to ensure the correctness and effectiveness of the algorithm, and to obtain the potential drug combination in the medical prescription.

Investigating Core Signaling Pathways of Hepatitis B Virus Pathogenesis for Biomarkers Identification and Drug Discovery via Systems Biology and Deep Learning Method.

Hepatitis B Virus (HBV) infection is a major cause of morbidity and mortality worldwide. However, poor understanding of its pathogenesis often gives rise to intractable immune escape and prognosis recurrence. Thus, a valid systematic approach based on big data mining and genome-wide RNA-seq data is imperative to further investigate the pathogenetic mechanism and identify biomarkers for drug design. In this study, systems biology method was applied to trim false positives from the host/pathogen genetic and epigenetic interaction network (HPI-GEN) under HBV infection by two-side RNA-seq data. Then, via the principal network projection (PNP) approach and the annotation of KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, significant biomarkers related to cellular dysfunctions were identified from the core cross-talk signaling pathways as drug targets. Further, based on the pre-trained deep learning-based drug-target interaction (DTI) model and the validated pharmacological properties from databases, i.e., drug regulation ability, toxicity, and sensitivity, a combination of promising multi-target drugs was designed as a multiple-molecule drug to create more possibility for the treatment of HBV infection. Therefore, with the proposed systems medicine discovery and repositioning procedure, we not only shed light on the etiologic mechanism during HBV infection but also efficiently provided a potential drug combination for therapeutic treatment of Hepatitis B.

ERROS DE PRESCRIÇÃO DE MEDICAMENTOS NA ATENÇÃO PRIMÁRIA FRENTE AO PROGRAMA NACIONAL DE SEGURANÇA DO PACIENTE

Erros de prescrição podem provocar riscos de dano ao paciente, além de gastos desnecessários ao paciente e ao sistema público de saúde. O objetivo deste estudo foi realizar revisão da literatura sobre os erros de prescrição na atenção primária à saúde frente ao Programa Nacional de Segurança do Paciente. Trata-se de estudo de revisão da literatura cuja estratégia de busca foi pesquisa por artigos brasileiros publicados no período de 2013 a 2018, período de vigência do Programa Nacional de Segurança do Paciente, utilizando os descritores prescrição de medicamentos, erros de medicação e atenção primária à saúde. Os erros mais frequentes foram omissão da duração do tratamento, medicamentos prescritos por nome comercial, falta de adesão à Relação Nacional de Medicamentos Essenciais, polifarmácia com interações medicamentosas potenciais, terapia duplicada e combinações contraindicadas de medicamentos. Ainda são escassos os estudos que investigam erros relacionados a prescrição de medicamentos no âmbito da atenção primária à saúde. Outrossim, apesar da existência de legislação que regula ações voltadas à segurança do paciente no Brasil, é necessária a sensibilização por parte dos profi ssionais de saúde e gerenciamento quanto ao cumprimento da legislação, o que pressupõe a necessidade de expansão dos Núcleos de Segurança do Paciente para o âmbito da Atenção Primária à Saúde.

A potential drug combination of omeprazole and patchouli alcohol significantly normalizes oxidative stress and inflammatory responses against gastric ulcer in ethanol-induced rat model

Omeprazole (OME) is a representative of proton pump inhibitors and widely used in anti-ulcer treatment. However, OME may cause some inevitable side effects and the long-term consequences of OME could increase the risk of diarrhea. Patchouli Alcohol (PA), the main extract of Pogostemonis Herba, have demonstrated benefits in treating gastric ulcer (GU) with low toxicity. The present study aimed to investigate the synergistically protective effects of OME and PA against ethanol-induced GU in rats to study the involvement of antioxidant and anti-inflammatory activities. Moreover, the anti-apoptosis, anti-oxidant and anti-inflammatory effects in H2O2-induced gastric epithelial cells (GES-1) and LPS-induced RAW264.7 cells were determined, as well as the modulation of signaling proteins. The results demonstrated that PA alone or combined with OME provided remarkable benefits by reducing ulcer areas, modulating oxidant stress and inflammatory factors and the therapeutic efficacy was showed to be dose-dependent, which were partly superior to that of high-dose OME only. Additionally, co-treated regimen could superiorly down-regulate cell apoptosis and regulate the levels of oxidant activities and inflammatory cytokines on H2O2-induced GES-1 cells and LPS-induced RAW264.7 cells, which involved with cleaved caspase 3, Bcl-2 and BAX protein expressions and MAPK pathway. We provided a new understanding that the combination of OME and PA possessed gastroprotective effects on modulating cell apoptosis, antioxidant stress and anti-inflammatory responses against GU. Therefore, PA was inferred to take a potential and critic role in gastric mucosa protection.

A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities

Personalized cancer treatments using combinations of drugs with a synergistic effect is attractive but proves to be highly challenging. Here we present an approach to uncover the efficacy of drug combinations based on the analysis of mono-drug effects. For this we used dose-response data from pharmacogenomic encyclopedias and represent these as a drug atlas. The drug atlas represents the relations between drug effects and allows to identify independent processes for which the tumor might be particularly vulnerable when attacked by two drugs. Our approach enables the prediction of combination-therapy which can be linked to tumor-driving mutations. By using this strategy, we can uncover potential effective drug combinations on a pan-cancer scale. Predicted synergies are provided and have been validated in glioblastoma, breast cancer, melanoma and leukemia mouse-models, resulting in therapeutic synergy in 75% of the tested models. This indicates that we can accurately predict effective drug combinations with translational value.

Therapeutic vulnerabilities among KRAS G12C mutant (mut) advanced cancers based on co-alteration (co-alt) patterns.

3625 Background: Oncogenic KRAS mut drive cancers and confer therapeutic resistance by activating MAPK signaling. Inhibiting KRAS has been elusive until the recent promising phase I trials with KRAS G12C inhibitors (i). We characterized frequencies of KRAS G12C mut and gene co-alt among advanced cancer patients (pts) to identify therapeutic vulnerabilities for combination development. Methods: We analyzed next generation sequencing datasets from MD Anderson Cancer Center (MDACC, n = 42,316) and AACR GENIE (n = 56,970). Genes and individual alterations were annotated for potential actionability with approved or investigational drugs and grouped into 12 oncogenic pathways. Frequencies of potential drug combinations with KRAS G12Ci were estimated per tumor type based on co-occurrence of potentially actionable alterations. Results: KRAS G12C was present in 850/34,801 (2.4%) advanced solid tumor and 22/7698 (0.3%) hematologic malignancy pts in MDACC dataset; and 1422 (2.5%) pts in AACR GENIE. Among solid tumor pts, 798 had histology data and 640 had ≥46 gene profiling. Most common cancers were non-small cell lung (NSCLC, 67%), colorectal (CRC, 24%), other gastrointestinal (oGI, 4%) and gynecologic (gyn, 2%). KRAS G12C prevalence was 19.5% (441/2265) in NSCLC and 4.2% (146/3469) in CRC. Genes most commonly co-altered were TP53 (42%), STK11 (17%) and MET (11%) in NSCLC; TP53 (58%), APC (54%) and PIK3CA (24%) in CRC; TP53 (42%), APC (21%) and ATM (21%) in oGI; TP53 (56%), PIK3CA (25%), and PTEN (19%) in gyn cancers. These co-alt did not impact overall survival. In both datasets, as compared to KRAS wild, KRAS G12C was significantly co-altered with STK11 in NSCLC; PIK3CA and SMAD4 in CRC (P &lt; 0.05 for all). EGFR mut in NSCLC and BRAF mut in CRC rarely co-occurred with KRAS G12C (P &lt; 0.01). Most frequently co-altered oncogenic pathways in NSCLC, CRC, oGI and gyn cancers respectively included PI3K (27, 32, 33, 44 %), receptor tyrosine kinases (13, 16, 42, 13 %) and DNA damage repair (12, 10, 38, 19 %). Potentially actionable co-alt frequencies suggest that combining KRAS G12Ci with mTORi or PI3Ki would be indicated most frequently, in 24% and 13% of all pts respectively. Conclusions: KRAS G12Ci development is most relevant for NSCLC, gastrointestinal and gyn cancers. The co-alt patterns highlight relevant oncogenic pathways and candidate drugs for future combination therapies. Co-inhibition of PI3K-mTOR and MAPK pathways has shown synergism in prior pre-clinical studies but had poor tolerance in pts. There is opportunity to revisit this approach with the new KRAS G12Ci.

Validation of a universal and highly sensitive two-dimensional liquid chromatography–tandem mass spectrometry methodology for the quantification of pyrazinamide, ethambutol, protionamide, and clofazimine in different biological matrices

A novel and potent anti-tuberculosis drug combination pyrazinamide (PZA), ethambutol (EMB), protionamide (PTO), and clofazimine (CFZ) that rapidly kills Mycobacterium tuberculosis (Mtb) in the lungs has been identified using the artificial-intelligence-enabled parabolic response surface approach. A universal and highly sensitive two-dimensional liquid chromatography-tandem mass spectrometry (2D-LC-MS/MS) method for the simultaneous determination of PZA, EMB, PTO, and CFZ in various biological samples in different states (liquid samples: plasma, bile, and urine; solid samples: tissue and feces) using simple pretreatment was established and validated. For the first dimension of this column-switching arrangement, the automated purification and enrichment of the drugs were achieved on a Polar-RP column. The subsequent analytical separation was performed on an Agilent Zorbax SB-Aq column, and the total loop time was 7.5 min. The positive-ionization mode with multiple reaction monitoring was used for detection. The sensitivity was good with no carry-over detected, and the lower limit of quantification ranged from 100 to 500 pg/mL. This quantification method was fully validated and proved to be robust in accordance with US Food and Drug Administration guidelines. High recoveries (85.3–111.4%) and accuracies (92.1–109.3%), together with high precision values (0.5–13.8%), were verified in all matrices. All standard curves showed favorable linearities with r2 > 0.995. This validated method was applied to study plasma pharmacokinetics, tissue distribution, and excretion in Sprague–Dawley rats after oral administration of the drug combination.