Mechanisms Of Drug Action Research Articles

Recent advances in protein precipitation-based methods for drug-target screening

Drug targets are biological macromolecules that bind drug molecules in vivo. Therefore, the system-wide identification of drug targets plays a vital role in fully understanding the mechanism of drug action, efficacy, and side effects. The unbiased screening of drug targets may accelerate the process of drug discovery and candidate screening. Mass spectrometry is a key tool for large-scale protein identification and accurate quantification owing to its high acquisition speed, resolution, and sensitivity. Mass spectrometry-based proteomics has been widely used for drug-target screening. It can systematically identify the protein-target landscape of a drug and elucidate drug-protein interactions. Commonly used drug-target characterization methods, such as labeling-based affinity enrichment, require the chemical derivatization of drug molecules, which is not only time-consuming but may also affect the affinity of the drug towards its targets. Furthermore, the spatial effects of the derivatization groups may block interactions between the drug and its targets. Considering the disadvantages of affinity-enrichment methods, strategies that do not require chemical derivatization have received widespread attention. Proteins may undergo denaturation, unfolding, and precipitation under different conditions such as high temperatures, extreme pH, denaturants, and mechanical stress. Binding to small-molecule drugs may alter the folding balance of target proteins. The conformational stability of target proteins can be stabilized by binding with drugs, and protein-drug complexes are more resistant than free proteins to the precipitation induced by different conditions. Based on this mechanism, various large-scale drug-target identification methods using protein precipitation have been developed by combining proteomics and mass spectrometry analysis, including thermal proteome profiling and solvent-, mechanical stress-, and pH-induced protein precipitation. These methods have been successfully applied to the characterization of small-molecule drug targets. In this review, we describe the protein precipitation-based methods used for the high-throughput discovery of drug targets and elucidation of the interactions between drugs and proteins in the past decade. We also summarize the characteristics of each method and discuss their application potential in drug-efficacy evaluation and drug discovery.

Computational Model Predicts Patient Outcomes in Luminal B Breast Cancer Treated with Endocrine Therapy and CDK4/6 Inhibition.

Development of a computational biomarker to predict, prior to treatment, the response to CDK4/6 inhibition (CDK4/6i) in combination with endocrine therapy in patients with breast cancer. A mechanistic mathematical model that accounts for protein signaling and drug mechanisms of action was developed and trained on extensive, publicly available data from breast cancer cell lines. The model was built to provide a patient-specific response score based on the expression of six genes (CCND1, CCNE1, ESR1, RB1, MYC, and CDKN1A). The model was validated in five independent cohorts of 148 patients in total with early-stage or advanced breast cancer treated with endocrine therapy and CDK4/6i. Response was measured either by evaluating Ki67 levels and PAM50 risk of relapse (ROR) after neoadjuvant treatment or by evaluating progression-free survival (PFS). The model showed significant association with patient's outcomes in all five cohorts. The model predicted high Ki67 [area under the curve; AUC (95% confidence interval, CI) of 0.80 (0.64-0.92), 0.81 (0.60-1.00) and 0.80 (0.65-0.93)] and high PAM50 ROR [AUC of 0.78 (0.64-0.89)]. This observation was not obtained in patients treated with chemotherapy. In the other cohorts, patient stratification based on the model prediction was significantly associated with PFS [hazard ratio (HR) = 2.92 (95% CI, 1.08-7.86), P = 0.034 and HR = 2.16 (1.02 4.55), P = 0.043]. A mathematical modeling approach accurately predicts patient outcome following CDK4/6i plus endocrine therapy that marks a step toward more personalized treatments in patients with Luminal B breast cancer.

Association of Antithrombotic Drug Use With Incident Intracerebral Hemorrhage Location.

Few population-based studies have assessed associations between the use of antithrombotic (platelet antiaggregant or anticoagulant) drugs and location-specific risks of spontaneous intracerebral hemorrhage (s-ICH). In this study, we estimated associations between antithrombotic drug use and the risk of lobar vs nonlobar incident s-ICH. Using Danish nationwide registries, we identified cases in the Southern Denmark Region of first-ever s-ICH in patients aged 50 years or older between 2009 and 2018. Each verified case was classified as lobar or nonlobar s-ICH and matched to controls in the general population by age, sex, and calendar year. Prior antithrombotic use was ascertained from a nationwide prescription registry. We calculated odds ratios (aORs) for associations between the use of clopidogrel, aspirin, direct oral anticoagulants (DOACs) or vitamin K antagonists (VKA), and lobar and nonlobar ICH in conditional logistic regression analyses that were adjusted for potential confounders. A total of 1,040 cases of lobar (47.9% men, mean age [SD] 75.2 [10.7] years) and 1,263 cases of nonlobar s-ICH (54.2% men, mean age 73.6 [11.4] years) were matched to 41,651 and 50,574 controls, respectively. A stronger association with lobar s-ICH was found for clopidogrel (cases: 7.6%, controls: 3.5%; aOR 3.46 [95% CI 2.45-4.89]) vs aspirin (cases: 22.9%, controls: 20.4%; aOR 2.14 [1.74-2.63; p = 0.019). Corresponding estimates for nonlobar s-ICH were not different between clopidogrel (cases: 5.4%, controls: 3.4%; aOR 2.44 [1.71-3.49]) and aspirin (cases: 20.7%, controls: 19.2%; aOR 1.77 [1.47-2.15]; p = 0.12). VKA use was associated with higher odds of both lobar (cases: 14.3%, controls: 6.1%; aOR 3.66 [2.78-4.80]) and nonlobar (cases: 15.4%, controls: 5.5%; aOR 4.62 [3.67-5.82]) s-ICH. The association of DOAC use with lobar s-ICH (cases: 3.5%, controls: 2.7%; aOR 1.66 [1.02-2.70]) was weaker than that of VKA use (p = 0.006). Corresponding estimates for nonlobar s-ICH were not different between DOACs (cases: 5.1%, controls: 2.4%; aOR 3.44 [2.33-5.08]) and VKAs (p = 0.20). Antithrombotics were associated with higher risks of s-ICH, but the strength of the associations varied by s-ICH location and drug, which may reflect differences in the cerebral microangiopathies associated with lobar vs nonlobar hemorrhages and the mechanisms of drug action.

Adjunctive Drug-Loaded Gel in the Treatment of Periodontitis: A Systematic Evaluation and Meta-Analysis

Background: This meta-analysis aimed to systematically investigate the efficacy of drug-loaded gel adjuncts in the treatment of periodontitis. Methods: A comprehensive search was conducted in six databases, i.e., the China National Knowledge Infrastructure, WanFang Data, VIP Chinese Science and Technology Periodical Database, China Biology Medicine Disc, Cochrane Library, PubMed, and Web of Science, from the inception until Jun 2023. The search focused on randomized controlled trials that examined the application of drug-loaded gels in the treatment of periodontitis. Periodontal probing depth and clinical attachment level were the primary and secondary outcomes, respectively. Stata 15.0 and Review Manager 5.4 were employed to perform the meta-analysis using the selected articles that met the predefined criteria. Results: This study included 16 randomized controlled trials involving 1146 participants. Subgroup analyses based on the follow-up period revealed that the gel-based drug-assisted subgingival root planning intervention had more favorable effects on periodontal probing depth (standardized mean difference=0.50, 95% confidence interval=[0.32, 0.68], I2=56.0%, P=0.001) and clinical attachment level (standardized mean difference=0.47, 95% confidence interval=[0.29, 0.66], I2=57.0%, P=0.0007) than the subgingival root planning intervention alone. However, subgroup analysis based on the action mechanism of gel drugs showed no statistically significant differences in periodontal probing depth and clinical attachment level between groups. Conclusion: The application of the drug-loaded gel as adjunctive therapy for periodontitis effectively reduced periodontal probing depth and promoted clinical attachment level recovery. The findings provide evidence-based support for the efficacy, security, and rational use of drug-loaded gel in the treatment of periodontitis

Cardiac Arrhythmia Risk after Anti-Cancer Drug Exposure and Related Disease Molecular Imaging Outlook: A Systematic Review, Meta-Analysis, and Network Meta-Analysis.

Chemotherapy is the main first-line treatment, but there is a problem of adverse reactions to systemic drugs. Chemotherapeutic agents may cause adverse effects on the body, influencing the prognosis. Whether the clinical application of anthracyclines is associated with an increased arrhythmic risk remains controversial. To evaluate the arrhythmic risk of anthracyclines as a class, and the comparative risk for each drug, we conducted a systematic review, meta-analysis, and network meta-analysis. PubMed, Web of Science, EMBASE, and the Cochrane Library were searched, up to March 2022, for randomized controlled trials, cohort studies, and case-control studies that investigated the association between anthracyclines treatment and the risk of arrhythmia. We followed the PRISMA 2020 guidelines for data selection and extraction. Outcomes were pooled using fixed effects models in cohort studies and randomized controlled studies, and random models in single-arm studies. Direct and indirect comparisons in network meta-analysis were performed using frequentist methods. In total, 4 cohort studies, 8 RCTs, and 18 single-arm studies were included in our analysis. Anthracyclines' use was associated with a statistically significant 90% increase in the risk of arrhythmia (odds ratio [OR] 1.90; 95% confidence interval [CI] 1.62-2.24) and a 114% increase in the risk of supraventricular arrhythmia (OR 2.14; 95% CI 1.18-3.89). And the single-arm studies also indicated that the incidence of arrhythmia rate is 20% and the 95% CI is 15/100-25/100. Epirubicin ranked most likely to have the highest risk of arrhythmia compared with non-anthracycline antineoplastic drugs in the analysis (OR 43.07 [95% CI 2.80-2105.83]) by network meta-analysis. Our findings show a significant association between anthracyclines' use and an increased risk of arrhythmia, especially supraventricular arrhythmia. Epirubicin ranked with the highest probability of arrhythmia. These results indicated that cardiac rhythm should be strictly monitored during the application of anthracyclines in clinical practice, and a possible therapy for anthracycline-associated arrhythmia should be explored. Molecular imaging technology is an important means to study the mechanism of drug action on cardiac electrophysiology in the future. By imaging molecular targets in cardiac cells, the effects of drugs on the electrophysiological properties of cardiac cells can be understood, which provides information for the development of safer and more effective drugs.

Therapeutic Approaches for Treatment and Management of Depressive Disorder: A Comprehensive Review

Depression, which is associated with high levels of cognitive and functional activity, melancholy, reduced activity, difficulty understanding, failure to focus, dietary abnormalities, sleep disturbances, and emotions of feelings of sadness, suicidal thoughts, and lack of hope are symptoms of a psychoneurotic disorder. It is a common and recurring disorder with a high morbidity and death rate throughout the world. Despite advances in depression treatment, such as selective serotonin reuptake inhibitors (SSRIs) and selective norepinephrine reuptake inhibitors (SNRIs), there remain several unmet clinical requirements in terms of effectiveness and adverse effects. These requirements range from enhanced to decreased side effects like emesis and erectile problems in treatment-resistant patients. There are a variety of combination therapies available to meet these needs. Several combination medicines and novel strategies have been found, all of which have the potential to improve one or so more areas. The kinds of targets and methods being used are extremely diverse. Therapeutic approaches, on the one hand, provide the benefits of SSRIs while including alternative routes in an attempt to improve efficacy or reduce undesirable effects. Neurotrophins (BDNF, IGF) are more novel targets on the other end of the spectrum, based on the most recent research suggesting antidepressants enhance neurogenesis. Antidepressants act by blocking many proteins, such as neurotransmitter transporters and G-protein coupled receptor families, in the brain. Some of the computational approaches that are useful for uncovering drug action mechanisms and are important for drug development are molecular docking, molecular dynamics, and drug candidates.

Application of network pharmacology in synergistic action of Chinese herbal compounds.

Herbal medicines are frequently blended in the form of multi-drug combinations primarily based on the precept of medicinal compatibility, to achieve the purpose of treating diseases. However, due to the lack of appropriate techniques and the multi-component and multi-target nature of Chinese medicine compounding, it is tough to explain how the drugs interact with each other. As a rising discipline, cyber pharmacology has formed a new approach characterized by using holistic and systematic "network targets" via the cross-fertilization of computer technology, bioinformatics, and different multidisciplinary disciplines. It can broadly screen the active ingredients of traditional Chinese medicine, enhance the effective utilization of drugs, and elucidate the mechanism of drug action. We will overview the principles of Chinese medicine compounding and dispensing, the research methods of network pharmacology, and the software of network pharmacology in the lookup of compounded Chinese medicines, aiming to supply thoughts for the better application of network pharmacology in the research of Chinese medicines.

Identifying and Exploring the Cognitive Nature of Threshold Concepts in Pharmacology to Improve Medical Students’ Learning

Phenomenon: Pharmacology is a fundamental healthcare discipline, but it can be difficult and counterintuitive for learners to learn. Navigation toward understanding pharmacology can be troublesome, but once the threshold to comprehension is crossed, learners can experience a transformative shift in their ways of thinking and practicing. We conducted an in-depth examination of threshold concepts within pharmacology, aiming to identify and prioritize their learning to improve the medical curriculum and enhance medical treatment and patient safety. Approach: We carried out a consensus generation process using the Nominal Group Technique (NGT) to identify potential threshold concepts in pharmacology. Participant groups of pharmacology experts and medical students considered, identified, reviewed, and ranked potential pharmacology threshold concepts within their own group. Then, using a logical, step-by-step approach, we combined the final ranked data from these multiple NGT sessions. We further analyzed these data using an abductive analysis approach; data were coded, categorized, reorganized, and conceptually mapped after critical evaluation. Conceptual themes were established corresponding to different phases of cognitive schema development. Findings: Six comprehensive conceptual themes were identified: Drug Mechanism of Action; Pharmacotherapeutics; Pharmacokinetics; Drug Receptor Interactions; Drug Terminology and Nomenclature; and Signaling Pathways. These concepts align with many of the key attributes of threshold concepts (e.g., troublesome, integrative and transformative). The cognitive schematic themes generated were (i) acquisition-troublesome; (ii) acquisition-transformative; (iii) automation-troublesome; (iv) automation-transformative. Insights: Transformative learning involves different stages of cognitive schema evolution, including acquisition, elaboration, and automation, and is influenced by both the inherent challenges of the concepts and limitations of human cognition. The high interactivity of these troublesome concepts challenge schema acquisition and automation. Troublesome concepts underpinning procedures or skills, while not easily explained by cognitive rules, can lead to slow, awkward, error-prone performance, creating additional barriers for practice. Integrating concepts into a coherent structure leads to the irreversible assimilation of knowledge and the transferability of both knowledge and skills, influencing learners’ epistemological transitions and ontological transformations at theoretical and professional levels. Further work on designing instructional models around assisting and automating schemas around identified troublesome knowledge, while addressing the impact of cognitive load, has the potential to promote transformational learning.

Use of Viscous medium to study anthelmintic drug action in Caenorhabditis elegans

Caenorhabditis elegans is an appealing tool for experimental evolution and for working with antiparasitic drugs, from understanding the molecular mechanisms of drug action and resistance to uncover new drug targets. We present a new methodology for studying the impact of antiparasitic drugs in C. elegans. Viscous medium was initially designed for C. elegans maintenance during long-term evolution experiments. Viscous medium provides a less structured environment than the standard nematode growth media agar, yet the bacteria food source remains suspended. Further, the Viscous medium offers the worm population enough support to move freely, mate, and reproduce at a rate comparable to standard agar cultures. Here, the Viscous medium was adapted for use in antiparasitic research. We observed a similar sensitivity of C. elegans to anthelmintic drugs as in standard liquid media and statistical difference to the standard agar media through a larval development assay. Using Viscous medium in C. elegans studies will considerably improve antiparasitic resistance research, and this medium could be used in studies aimed at understanding long-term multigenerational drug activity.

Twenty-five years with HER2 targeted therapy.

The development of trastuzumab is among the most significant cancer drug development projects in the 20th century. Trastuzumab became a gamechanger for the treatment of human epidermal growth receptor 2 (HER2) positive breast cancer, with a significant positive impact on disease recurrence and survival. The development of trastuzumab was the beginning of a new era of cancer drug development, which showed us the importance of understanding the molecular pathophysiology and drug mechanism of action. The drug-diagnostic codevelopment model, in which the drug is developed in parallel with a predictive biomarker assay, has had a significant impact on today's cancer drug development, and we are indebted to trastuzumab when it comes to the clinical enrichment trial design. Trastuzumab is not the only drug developed to target the HER2 protein. Over the past few decades, several new HER2 targeted therapies have been developed, including small-molecule tyrosine kinase inhibitors (TKI), monoclonal antibodies, and antibody-drug conjugates (ADC). In particular, the ADC trastuzumab deruxtecan seems to pave new avenues when it comes to HER2 targeted treatment not only for breast cancer, but also for gastric cancer and non-small cell lung cancer. With the development of trastuzumab as a reference point, this article will provide a brief summary of the efficacy of HER2 targeted therapy, including testing for HER2 positivity, as it has evolved over the past 25 years.

Computational Insight into the Mechanism of Action of DNA Gyrase Inhibitors; Revealing a New Mechanism.

Discovery of novel antimicrobial agents is in need to deal with antibiotic resistance. Elucidating the mechanism of action for established drugs contributes to this endeavor. DNA gyrase is a therapeutic target used in the design and development of new antibacterial agents. Selective antibacterial gyrase inhibitors are available; however, resistance development against them is a big challenge. Hence, novel gyrase inhibitors with novel mechanisms are required. In this study, the mechanism of action for selected DNA gyrase inhibitors available was carried out through molecular docking and molecular dynamics (MD) simulation. In addition, pharmacophore analysis, density functional theory (DFT) calculations, and computational pharmacokinetics analysis of the gyrase inhibitors were performed. This study demonstrated that all the DNA gyrase inhibitors investigated, except compound 14, exhibit their activity by inhibiting gyrase B at a binding pocket. The interaction of the inhibitors at Lys103 was found to be essential for the binding. The molecular docking and MD simulation results revealed that compound 14 could act by inhibiting gyrase A. A pharmacophore model that consisted of the features that would help the inhibition effect was generated. The DFT analysis demonstrated 14 had relatively high chemical stability. Computational pharmacokinetics analysis revealed that most of the explored inhibitors were estimated to have good drug-like properties. Furthermore, most of the inhibitors were found to be non-mutagenic. In this study, mode of action elucidation through molecular docking and MD simulation, pharmacophore model generation, pharmacokinetic property prediction, and DFT study for selected DNA gyrase inhibitors were carried out. The outcomes of this study are anticipated to contribute to the design of novel gyrase inhibitors.

Proteomic Profiling of Antimalarial Plasmodione Using 3-Benz(o)ylmenadione Affinity-Based Probes.

Understanding the mechanisms of drug action in malarial parasites is crucial for the development of new drugs to combat infection and to counteract drug resistance. Proteomics is a widely used approach to study host-pathogen systems and to identify drug protein targets. Plasmodione is an antiplasmodial early-lead drug exerting potent activities against young asexual and sexual blood stages in vitro with low toxicity to host cells. To elucidate its molecular mechanisms, an affinity-based protein profiling (AfBPP) approach was applied to yeast and P. falciparum proteomes. New (pro-) AfBPP probes based on the 3-benz(o)yl-6-fluoro-menadione scaffold were synthesized. With optimized conditions of both photoaffinity labeling and click reaction steps, the AfBPP protocol was then applied to a yeast proteome, yielding 11 putative drug-protein targets. Among these, we found four proteins associated with oxidoreductase activities, the hypothesized type of targets for plasmodione and its metabolites, and other proteins associated with the mitochondria. In Plasmodium parasites, the MS analysis revealed 44 potential plasmodione targets that need to be validated in further studies. Finally, the localization of a 3-benzyl-6-fluoromenadione AfBPP probe was studied in the subcellular structures of the parasite at the trophozoite stage.

Prescription Data Mining and Network Pharmacology Study of 1152 Patients with Rectal Prolapse Using Traditional Chinese Medicine.

In recent years, the incidence of rectal prolapse has increased significantly due to the sedentary lifestyle and irregular eating habits of modern life. However, there is a lack of clinical studies on the treatment of rectal prolapse with traditional Chinese medicine (TCM) with a large sample size. Therefore, this study investigated the characteristics of rectal prolapse treatment formulas and then studied the network pharmacology of their core therapeutic drugs, which can help to provide a reference for the treatment and postoperative care of rectal prolapse patients. This study aimed to explore the prescription characteristics and the mechanism of action of core drugs in the treatment of rectal prolapse in Chinese medicine through data mining and bioinformatics techniques. We collected the diagnosis and treatment information of patients with rectal prolapse from January 2014 to September 2021 in the electronic case database of Nanjing Hospital of TCM, mined the patient information and prescription features using R, screened the active ingredients of the core pairs of drugs and disease drug intersection targets using TCMSP and GnenCard databases, and constructed a Protein-protein interaction (PPI) network using STRING and Cytoscape, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the intersecting targets were performed using Metascape and R. We found that prolapse is easy to occur in people over 50 years old, preferably in autumn and winter. Commonly used therapeutic Chinese medicines include Glycyrrhiza glabra, Radix angelicae sinensis, Radix astragali, Atractylodes macrocephala, and Pericarpium citri reticulatae, which are mostly deficiency tonic medicines, warm in nature, and belong to spleen meridian. The core therapeutic medicinal pair was "Bupleuri radix-Cimicifugae rhizoma". There were 190 common targets of Bupleuri radix and Cimicifugae rhizoma, and 71 intersection targets of the drug pair and prolapse. The main components of the core drugs for the treatment of prolapse may be quercetin, kaempferol, Stigmasterol, etc, and the core targets may be CASP3, AKT1, HIF1A, etc. The total number of GO entries for the intersection targets of "Bupleuri radix-Cimicifugae rhizoma" and diseases was 3495, among which the molecular functions accounted for the largest proportion, mainly Pathways in cancer, IL-18 signaling pathway, etc. KEGG enriched pathway analysis yielded 168 results, and the major pathways were pathways in cancer, lipid and atherosclerosis, IL-17 signaling pathway, etc. Conclusion: This study adopted real-world research methodology and used data mining and bioinformatics technology to mine the medication law of rectal prolapse and its core drug action mechanism from the clinical information of Chinese medicine.

A literature review on pharmacological aspects, docking studies, and synthetic approaches of quinazoline and quinazolinone derivatives.

Quinazoline and quinazolinone derivatives piqued medicinal chemistry interest in developing novel drug candidates owing to their pharmacological potential. They are important chemicals for the synthesis of a variety of physiologically significant and pharmacologically useful molecules. Quinazoline and quinazolinone derivatives have anticancer, anti-inflammatory, antidiabetic, anticonvulsant, antiviral, and antimicrobial potential. The increased understanding of quinazoline and quinazolinone derivatives in biological activities provides opportunities for new medicinal products. The present review focuses on novel advances in the synthesis of these important scaffolds and other medicinal aspects involving drug design, structure-activity relationship, and action mechanisms of quinazoline and quinazolinone derivatives to help in the development of new quinazoline and quinazolinone derivatives.

Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization

Due to cancer’s complex nature and variable response to therapy, precision oncology informed by omics sequence analysis has become the current standard of care. However, the amount of data produced for each patient makes it difficult to quickly identify the best treatment regimen. Moreover, limited data availability has hindered computational methods’ abilities to learn patterns associated with effective drug-cell line pairs. In this work, we propose the use of contrastive learning to improve learned drug and cell line representations by preserving relationship structures associated with drug mechanisms of action and cell line cancer types. In addition to achieving enhanced performance relative to a state-of-the-art method, we find that classifiers using our learned representations exhibit a more balanced reliance on drug- and cell line-derived features when making predictions. This facilitates more personalized drug prioritizations that are informed by signals related to drug resistance.

ScRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.

Decrypting the molecular basis of cellular drug phenotypes by dose-resolved expression proteomics.

Proteomics is making important contributions to drug discovery, from target deconvolution to mechanism of action (MoA) elucidation and the identification of biomarkers of drug response. Here we introduce decryptE, a proteome-wide approach that measures the full dose-response characteristics of drug-induced protein expression changes that informs cellular drug MoA. Assaying 144 clinical drugs and research compounds against 8,000 proteins resulted in more than 1 million dose-response curves that can be interactively explored online in ProteomicsDB and a custom-built Shiny App. Analysis of the collective data provided molecular explanations for known phenotypic drug effects and uncovered new aspects of the MoA of human medicines. We found that histone deacetylase inhibitors potently and strongly down-regulated the T cell receptor complex resulting in impaired human T cell activation in vitro and ex vivo. This offers a rational explanation for the efficacy of histone deacetylase inhibitors in certain lymphomas and autoimmune diseases and explains their poor performance in treating solid tumors.

Evaluation of indole-based organometallics as transfer hydrogenation catalysts with anticancer activity

Half-sandwich complexes containing transition metals offer a wide range of applications owing to their interactions with a variety of targets/molecules that are not accessible for some organic scaffolds. The use of such complexes is therefore a way to provide new mechanisms of drug action. Here, we report a series of neutral metal complexes of the type [(arene)M(O-cyclohexyl-1H-indole-2-carbothiolate)Cl] that have the ability to reduce the coenzyme NAD+ at physiological conditions in the presence of sodium formate as an hydride donor. The influence of the metal ion (Ru, Os, Rh, Ir) is studied to establish some structure-activity relationships. The [(η6-p-cym)Ru(O-cyclohexyl-1H-indole-2-carbothioate)Cl] is the most active catalyst of this family of complexes with a turnover frequency (TOF) of 14.79 h-1 for the transfer hydrogenation reaction of NAD+ to give 1,4-NADH in the presence of formate. Further in vitro studies were carried out to determine a relationship between catalytic activity and potency as an anticancer drug, with the rhodium complex promisingly showing 7x more selectivity towards cancer cells (A2780) than normal cells (PNT2). Preliminary in vivo studies demonstrated the importance of co-treatment with formate to enhance activity. This work gives insights into the mechanism of action of this family of indole-based organometallic complexes.

PBAC: A pathway-based attention convolution neural network for predicting clinical drug treatment responses.

Precise and personalized drug application is crucial in the clinical treatment of complex diseases. Although neural networks offer a new approach to improving drug strategies, their internal structure is difficult to interpret. Here, we propose PBAC (Pathway-Based Attention Convolution neural network), which integrates a deep learning framework and attention mechanism to address the complex biological pathway information, thereby provide a biology function-based robust drug responsiveness prediction model. PBAC has four layers: gene-pathway layer, attention layer, convolution layer and fully connected layer. PBAC improves the performance of predicting drug responsiveness by focusing on important pathways, helping us understand the mechanism of drug action in diseases. We validated the PBAC model using data from four chemotherapy drugs (Bortezomib, Cisplatin, Docetaxel and Paclitaxel) and 11 immunotherapy datasets. In the majority of datasets, PBAC exhibits superior performance compared to traditional machine learning methods and other research approaches (area under curve = 0.81, the area under the precision-recall curve = 0.73). Using PBAC attention layer output, we identified some pathways as potential core cancer regulators, providing good interpretability for drug treatment prediction. In summary, we presented PBAC, a powerful tool to predict drug responsiveness based on the biology pathway information and explore the potential cancer-driving pathways.

Design, Synthesis, and Investigation of the Pharmacokinetics and Anticancer Activities of Indenoisoquinoline Derivatives That Stabilize the G-Quadruplex in the MYC Promoter and Inhibit Topoisomerase I.

G-quadruplexes are noncanonical four-stranded DNA secondary structures. MYC is a master oncogene and the G-quadruplex formed in the MYC promoter functions as a transcriptional silencer and can be stabilized by small molecules. We have previously revealed a novel mechanism of action for indenoisoquinoline anticancer drugs, dual-downregulation of MYC and inhibition of topoisomerase I. Herein, we report the design and synthesis of novel 7-aza-8,9-methylenedioxyindenoisoquinolines based on desirable substituents and π-π stacking interactions. These compounds stabilize the MYC promoter G-quadruplex, significantly lower MYC levels in cancer cells, and inhibit topoisomerase I. MYC targeting was demonstrated by differential activities in Raji vs CA-46 cells and cytotoxicity in MYC-dependent cell lines. Cytotoxicities in the NCI-60 panel of human cancer cell lines were investigated. Favorable pharmacokinetics were established, and in vivo anticancer activities were demonstrated in xenograft mouse models. Furthermore, favorable brain penetration, brain pharmacokinetics, and anticancer activity in an orthotopic glioblastoma mouse model were demonstrated.