Bacterial Metabolism Affects the C. elegans Response to Cancer Chemotherapeutics.

The human microbiota greatly affects physiology and disease; however, the contribution of bacteria to the response to chemotherapeutic drugs remains poorly understood. Caenorhabditis elegans and its bacterial diet provide a powerful system to study host-bacteria interactions. Here, we use this system to study how bacteria affect the C. elegans response to chemotherapeutics. We find that different bacterial species can increase the response to one drug yet decrease the effect of another. We perform genetic screens in two bacterial species using three chemotherapeutic drugs: 5-fluorouracil (5-FU), 5-fluoro-2'-deoxyuridine (FUDR), and camptothecin (CPT). We find numerous bacterial nucleotide metabolism genes that affect drug efficacy in C. elegans. Surprisingly, we find that 5-FU and FUDR act through bacterial ribonucleotide metabolism to elicit their cytotoxic effects in C. elegans rather than by thymineless death or DNA damage. Our study provides a blueprint for characterizing the role of bacteria in the host response to chemotherapeutics.

Deciphering single-cell gene expression variability and its role in drug response.

The effectiveness of drug treatments is profoundly influenced by individual responses, which are shaped by gene expression variability, particularly within pharmacogenes. Leveraging single-cell RNA sequencing (scRNA-seq) data, our study explores the extent of expression variability among pharmacogenes in a wide array of cell types across eight different human tissues, shedding light on their impact on drug responses. Our findings broaden the established link between variability in pharmacogene expression and drug efficacy to encompass variability at the cellular level. Moreover, we unveil a promising approach to enhance drug efficacy prediction. This is achieved by leveraging a combination of cross-cell and cross-individual pharmacogene expression variation measurements. Our study opens avenues for more precise forecasting of drug performance, facilitating tailored and more effective treatments in the future.

Unraveling the mechanism of carbonic anhydrase IX inhibition by alkaloids from Ruta chalepensis: A synergistic analysis of in vitro and in silico data.

Due to the pivotal role of carbonic anhydrase IX (CA IX) in pathological conditions, there's a pressing need for novel inhibitors to improve patient outcomes and clinical management. Herein, we investigated the inhibitory efficacy of six alkaloids from Ruta chalepensis against CA IX through in vitro inhibition assay and computational modeling. Skimmianine and maculosidine displayed significant inhibitory activity in vitro, with low IC50 values of 105.2 ± 3.2 and 295.7 ± 14.1 nM, respectively. Enzyme kinetics analyses revealed that skimmianine exhibited a mixed inhibition mode, contrasting with the noncompetitive inhibition mechanism observed for the reference drug (acetazolamide), as indicated by intersecting lines in the Lineweaver-Burk plots. The findings of docking calculations revealed that skimmianine and maculosidine exhibited extensive polar interactions with the enzyme. These alkaloids demonstrate substantial binding interactions and occupy identical binding site as acetazolamide, thereby enhancing their efficacy as inhibitors of CA IX. Utilizing a 100 ns molecular dynamics (MD) simulation, the dynamic interactions between isolated alkaloids and CA IX were intensively assessed. Analysis of diverse MD parameters revealed that skimmianine and maculosidine displayed consistent trajectories and notable energy stabilization during their interaction with CA IX. The findings of MM/PBSA analysis depicted the minimum binding free energy for skimmianine and maculosidine. In addition, the Potential Energy Landscape (PEL) analysis revealed distinct and stable conformational states for the CA IX-ligand complexes, with Skimmianine showing the most stable and lowest energy configuration. These computational findings align with experimental results, emphasizing the potential efficacy of skimmianine and maculosidine as inhibitors of CA IX.

VERU-111, an Orally Available Tubulin Inhibitor, Suppresses Ovarian Tumor Growth and Metastasis.

Ovarian cancer is the most lethal gynecological malignancy, with a 5-year survival rate of approximately 50%. The dismal prognosis is due in part to metastatic disease and acquired drug resistance to conventional chemotherapies such as taxanes. Colchicine binding site inhibitors (CBSIs) are attractive alternatives to taxanes because they could potentially achieve oral bioavailability and overcome drug resistance associated with the prolonged use of taxanes. VERU-111 is one of the most advanced CBSIs that is orally available, potent, well-tolerated, and has shown good efficacy in several preclinical solid tumor models. Here, we demonstrate for the first time the in vitro potency of VERU-111 as well as its efficacy at inhibiting tumor growth and metastasis in an orthotopic ovarian cancer mouse model. VERU-111 has nanomolar potency against ovarian cancer cell lines and strongly inhibits colony formation, proliferation, invasion, and migration. VERU-111 disrupts microtubule formation to induce mitotic catastrophe and, ultimately, apoptosis in a concentration-dependent manner. The efficacy of VERU-111 was comparable with standard chemotherapy paclitaxel, the current first-line treatment for ovarian cancer, with no observed synergy with combination paclitaxel + VERU-111 treatment. In vivo, VERU-111 markedly suppressed ovarian tumor growth and completely suppressed distant organ metastasis. Together, these results support VERU-111 for its potential as a novel therapy for ovarian cancer, particularly for late-stage metastatic disease. Significance Statement VERU-111 is an investigational new drug and has comparable efficacy as paclitaxel in suppressing tumor cell proliferation, colony formation, and migration in ovarian cancer models in vitro and has potent in vivo anti-tumor and anti-metastatic activity in an orthotopic ovarian cancer mouse model. VERU-111 has low systemic toxicity and, unlike paclitaxel, is orally bioavailable and is not a substrate for the major drug efflux transporters, making it a promising and attractive alternative to taxane-based therapy.

Pharmacometabolomics in Drug Disposition, Toxicity and Precision Medicine.

The precision medicine initiative has driven a substantial change in the way scientists and health care practitioners think about diagnosing and treating disease. While it has long been recognized that drug response is determined by the intersection of genetic, environmental and disease factors, improvements in technology have afforded precision medicine guided dosing of drugs to improve efficacy and reduce toxicity. Pharmacometabolomics aims to evaluate small molecule metabolites in plasma and/or urine to help evaluate mechanisms that predict and/or reflect drug efficacy and toxicity. In this mini review, we provide an overview of pharmacometabolomic approaches and methodologies. Relevant examples where metabolomic techniques have been used to better understand drug efficacy and toxicity in major depressive disorder and cancer chemotherapy are discussed. In addition, the utility of metabolomics in drug development and understanding drug metabolism, transport and pharmacokinetics is reviewed. Pharmacometabolomic approaches can help understand factors mediating drug disposition, efficacy and toxicity. While important advancements in this area have been made, their remain several challenges that must be overcome before this approach can be fully implemented into clinical drug therapy. Significance Statement Pharmacometabolomics has emerged as an approach to identify metabolites that allow for implementation of precision medicine approaches to pharmacotherapy. This review article provides an overview pharmacometabolomics including highlights of important examples.

Bohemian Rhapsody of Future Drug Delivery Systems: Rational Changes Necessary for the Next Revolution.

Controlled drug delivery technology has matured for more than 70 years, starting from a twice-a-day oral formulation to 6 month long-acting injectable formulations. Further technological advances require superior formulations to treat various diseases more efficiently. Developing future formulations with practical innovations for treating existing and new diseases necessitates our continued efforts to overcome at least three main hurdles. They include (i) drug delivery with reduced side effects, (ii) long-term treatment of chronic diseases, and (iii) the overcoming of biological barriers. Such efforts start with the improved ability to accurately test drug delivery efficacy using proper controls. Future development can be aided by artificial intelligence if used properly. The next revolution of drug delivery systems will be augmented if implementation is given equal weight as discovery. Such a process can be accelerated with the systemic revamp of the research funding structure and cultivating a new generation of scientists who can think differently.

Efficacy and safety of chloroquine plus primaquine for the treatment of Plasmodium vivax malaria in Hamusit site, Northwestern Ethiopia.

BACKGROUND: Plasmodium vivax malaria is still an important public health problem in Ethiopia. Unlike Plasmodium falciparum, P. vivax has a dormant liver stage (hypnozoite) that can be a risk of recurrent vivax malaria unless treated by radical cure with primaquine. Drug resistance to chloroquine is threatening malaria control and elimination efforts. This study assessed the therapeutic efficacy and safety of chloroquine plus 14 days of primaquine on P. vivax infection based on parasitological, clinical, and haematological parameters. METHODS: A single-arm in vivo prospective therapeutic efficacy study was conducted to assess the clinical and parasitological response to the first-line treatment of P. vivax in Ethiopia, chloroquine plus 14 days low dose of (0.25 mg/kg/day) primaquine between December 2022 and March 2023 at Hamusit Health Centre using the standard World Health Organization (WHO) protocol. A total of 100 study participants with P. vivax mono-infection who were over 6 months old were enrolled and monitored for adequate clinical and parasitological responses for 42 days. The WHO double-entry Excel sheet and SPSS v.25 software were used for Kaplan-Meier survival analysis, and a paired t-test was used for analysis of haemoglobin improvements between follow up days. RESULTS: A total of 100 patients were enrolled among those, 96% cases were rural residents, 93% had previous malaria exposure, and predominant age group was 5-15 years (61%). 92.6% (95% CI 85.1-96.4%) of enrolled patients were adequate clinical and parasitological response, and 7.4% (95% CI 3.6-14.9%) recurrences were observed among treated patients. The fever and parasite clearance rate on day 3 were 98% and 94%, respectively. The baseline haemoglobin levels improved significantly compared to those days 14 and 42 (p < 0.001). No serious adverse event was observed during the study period. CONCLUSIONS: In this study, co-administration of chloroquine with primaquine was efficacious and well-tolerated with fast resolution of fever and high parasites clearance rate. However, the 7.4% failure is reported is alarming that warrant further monitoring of the therapeutic efficacy study of P. vivax.

Hypoxia-driven lncRNA CTD-2510F5.4: a potential player in hepatocellular carcinoma's prognostic stratification, cellular behavior, tumor microenvironment, and therapeutic response.

BACKGROUND: Hepatocellular carcinoma (HCC) is a highly aggressive cancer with limited therapeutic options. Hypoxia is a common feature of the tumor microenvironment that reportedly promotes tumorigenesis. Long non-coding RNAs (lncRNAs) are a class of regulatory molecules with diverse functions in cancer biology. This study aimed to identify hypoxia-induced lncRNAs associated with HCC and evaluate their potential as prognostic and therapeutic biomarkers. METHODS: We employed microarray and The Cancer Genome Atlas (TCGA) data to identify hypoxia-induced lncRNAs in HCC. Subsequently, we focused on CTD-2510F5.4, a candidate lncRNA, and predicted its functional roles in HCC using Gene Ontology (GO) and Guilt-by-Association (GBA) analyses. We validated its expression under hypoxia in Huh7 and HepG2 cells using RT-PCR. Functional assays, including CCK8, wound-healing, and transwell assays, were performed to assess the effects of CTD-2510F5.4 overexpression on HCC cell proliferation, invasion, and metastasis potential. Furthermore, we investigated the association between CTD-2510F5.4 expression and patient prognosis, tumor mutation signature, immune microenvironment characteristics, and therapeutic response to different treatment modalities. RESULTS: Our data demonstrated a significant upregulation of CTD-2510F5.4 expression in response to hypoxia. Functional enrichment analyses revealed the involvement of CTD-2510F5.4 in cell cycle regulation, E2F targets, G2M checkpoint control, and MYC signaling pathways. Functionally, CTD-2510F5.4 overexpression promoted HCC cell proliferation, invasion, and metastasis. Patients with high CTD-2510F5.4 expression exhibited a worse prognosis, a higher prevalence of TP53 mutations, increased infiltration by immunosuppressive regulatory T cells, elevated expression of immune checkpoint molecules, and higher TIDE scores indicative of immune dysfunction and exclusion. Notably, patients with low CTD-2510F5.4 expression displayed greater sensitivity to immunotherapy and antiangiogenic therapy, while those with high expression responded better to chemotherapy. CONCLUSION: Our findings suggest that CTD-2510F5.4 plays a critical role in HCC progression and immune modulation. Its potential as a prognostic biomarker and a predictor of therapeutic response warrants further investigation for personalized treatment strategies in HCC patients.

A novel approach to investigate severe asthma and COPD: the 3d ex vivo respiratory mucosa model.

Purpose: This article illustrates the replication of asthma and COPD conditions in a laboratory setting and the potential applications of this methodology.Introduction: Biologic drugs have been shown to enhance the treatment of severe asthma and COPD. Monoclonal antibodies against specific targets have dramatically changed the management of these conditions. Although the inflammatory pathways of asthma and COPD have already been clearly outlined, alternative mechanisms of action remain mostly unexplored. They could provide additional insights into these diseases and their clinical management.Aims: In vivo or in vitro models have thus been developed to test alternative hypotheses. This study describes sophisticated ex vivo models that mimic the response of human respiratory mucosa to disease triggers, aiming to narrow the gap between laboratory studies and clinical practice.Results: These models successfully replicate crucial aspects of these diseases, such as inflammatory cell presence, cytokine production, and changes in tissue structure, offering a dynamic platform for investigating disease processes and evaluating potential treatments, such as monoclonal antibodies. The proposed models have the potential to enhance personalized medicine approaches and patient-specific treatments, helping to advance the understanding and management of respiratory diseases.

A comprehensive review on anticancer evaluation techniques.

The development of effective anticancer strategies and the improvement of our understanding of cancer need analytical tools. Utilizing a variety of analytical approaches while investigating anti-cancer medicines gives us a thorough understanding of the traits and mechanisms concerned to cancer cells, which enables us to develop potent treatments to combat them. The importance of anticancer research may be attributed to various analytical techniques that contributes to the identification of therapeutic targets and the assessment of medication efficacy, which are crucial things in expanding our understanding of cancer biology. The study looks at methods that are often used in cancer research, including cell viability assays, clonogenic assay, flow cytometry, 2D electrophoresis, microarray, immunofluorescence, western blot caspase activation assay, bioinformatics, etc. The fundamentals, applications, and how each technique analytical advances our understanding of cancer are briefly reviewed.

Revolutionizing control strategies against Mycobacterium tuberculosis infection through selected targeting of lipid metabolism.

Lipid species play a critical role in the growth and virulence expression of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB). During Mtb infection, foamy macrophages accumulate lipids in granulomas, providing metabolic adaptation and survival strategies for Mtb against multiple stresses. Host-derived lipid species, including triacylglycerol and cholesterol, can also contribute to the development of drug-tolerant Mtb, leading to reduced efficacy of antibiotics targeting the bacterial cell wall or transcription. Transcriptional and metabolic analyses indicate that lipid metabolism-associated factors of Mtb are highly regulated by antibiotics and ultimately affect treatment outcomes. Despite the well-known association between major antibiotics and lipid metabolites in TB treatment, a comprehensive understanding of how altered lipid metabolites in both host and Mtb influence treatment outcomes in a drug-specific manner is necessary to overcome drug tolerance. The current review explores the controversies and correlations between lipids and drug efficacy in various Mtb infection models and proposes novel approaches to enhance the efficacy of anti-TB drugs. Moreover, the review provides insights into the efficacious control of Mtb infection by elucidating the impact of lipids on drug efficacy. This review aims to improve the effectiveness of current anti-TB drugs and facilitate the development of innovative therapeutic strategies against Mtb infection by making reverse use of Mtb-favoring lipid species.

Machine learning based identification potential feature genes for prediction of drug efficacy in nonalcoholic steatohepatitis animal model.

BACKGROUND: Nonalcoholic Steatohepatitis (NASH) results from complex liver conditions involving metabolic, inflammatory, and fibrogenic processes. Despite its burden, there has been a lack of any approved food-and-drug administration therapy up till now. PURPOSE: Utilizing machine learning (ML) algorithms, the study aims to identify reliable potential genes to accurately predict the treatment response in the NASH animal model using biochemical and molecular markers retrieved using bioinformatics techniques. METHODS: The NASH-induced rat models were administered various microbiome-targeted therapies and herbal drugs for 12 weeks, these drugs resulted in reducing hepatic lipid accumulation, liver inflammation, and histopathological changes. The ML model was trained and tested based on the Histopathological NASH score (HPS); while (0-4) HPS considered Improved NASH and (5-8) considered non-improved, confirmed through rats' liver histopathological examination, incorporates 34 features comprising 20 molecular markers (mRNAs-microRNAs-Long non-coding-RNAs) and 14 biochemical markers that are highly enriched in NASH pathogenesis. Six different ML models were used in the proposed model for the prediction of NASH improvement, with Gradient Boosting demonstrating the highest accuracy of 98% in predicting NASH drug response. FINDINGS: Following a gradual reduction in features, the outcomes demonstrated superior performance when employing the Random Forest classifier, yielding an accuracy of 98.4%. The principal selected molecular features included YAP1, LATS1, NF2, SRD5A3-AS1, FOXA2, TEAD2, miR-650, MMP14, ITGB1, and miR-6881-5P, while the biochemical markers comprised triglycerides (TG), ALT, ALP, total bilirubin (T. Bilirubin), alpha-fetoprotein (AFP), and low-density lipoprotein cholesterol (LDL-C). CONCLUSION: This study introduced an ML model incorporating 16 noninvasive features, including molecular and biochemical signatures, which achieved high performance and accuracy in detecting NASH improvement. This model could potentially be used as diagnostic tools and to identify target therapies.

Plasma metabolites and inflammatory proteins profiling predict outcome of Fufang Duzhong Jiangu granules treating Kashin-Beck disease.

To investigate predictive biomarkers that could be used to identify patients' response to treatment, plasma metabolomics and proteomics analyses were performed in Kashin-Beck disease (KBD) patients treated with Fufang Duzhong Jiangu Granules (FDJG). Plasma was collected from 12 KBD patients before treatment and 1 month after FDJG treatment. LC-MS and olink proteomics were employed for obtaining plasma metabolomics profiling and inflammatory protein profiles. Patients were classified into responders and non-responders based on drug efficacy. Enrichment analyses of differential metabolites and proteins of the responders at baseline and after treatment were conducted to study the mechanism of drug action. Differential metabolites and proteins between the two groups were screened as biomarkers to predict the drug efficacy. The receiver operating characteristic curve was used to evaluate the prediction accuracy of biomarkers. The changes in metabolites and inflammatory proteins in responders after treatment reflected the mechanism of FDJG treatment for KBD, which may act on glycerophospholipid metabolism, d-glutamine and d-glutamate metabolism, nitrogen metabolism and NF-kappa B signaling pathway. Three metabolites were identified as potential predictors: N-undecanoylglycine, ß-aminopropionitrile and PC [18:3(6Z,9Z,12Z)/20:4(8Z,11Z,14Z,17Z)]. For inflammatory protein, interleukin-8 was identified as a predictive biomarker to detect responders. Combined use of these four biomarkers had high predictive ability (area under the curve = 0.972).

Malignancy progression and treatment efficacy estimation of osteosarcoma patients based on in vitro cell culture model and analysis.

BACKGROUND: Osteosarcoma (OS) usually happens in patients under 20 years old and is notorious for its low survivorship and limb loss. Personalized medicine is a viable approach to increase the efficiency of chemotherapy which is the main prognostic factor for survivorship after surgical treatment. METHODS: In this five-year prospective observational study, we collected primary cells of osteosarcoma from 15 patients, and examined the correlation between clinical characters of patients and cell properties characterized using various in vitro assays. The properties including genes expression, pro-angiogenic capability and anti-cancer drug response are characterized respectively by using RT-PCR, tube formation assay, osteogenesis assay and drug testing on 3D tumor spheroid model. RESULT: The results suggest that OS patients with higher MMP9 expression levels have higher probability to develop skip metastasis (p = 0.041). The 3D tumor spheroid test based on the median lethal dose from 2D culture provides some prognostic value. Patients do not response well to methotrexate (MTX) show higher percentage of high pathology grade (p = 0.009) and lung metastasis (p = 0.044). Also, patients respond well to ifosfamide (IFO) have higher probability to achieve high tumor necrosis rate (p = 0.007). CONCLUSION: The association between cell properties and clinical characters of patients provided by our data can act as potential prognostic factors to help physicians to develop effective personalized chemotherapy for osteosarcoma treatments.

Patient-derived organoids for precision oncology: a platform to facilitate clinical decision making.

BACKGROUND: Despite recent advances in research, there are still critical lacunae in our basic understanding of the cause, pathogenesis, and natural history of many cancers, especially heterogeneity in patient response to drugs and mediators in the transition from malignant to invasive phenotypes. The explication of the pathogenesis of cancer has been constrained by limited access to patient samples, tumor heterogeneity and lack of reliable biological models. Amelioration in cancer treatment depends on further understanding of the etiologic, genetic, biological, and clinical heterogeneity of tumor microenvironment. Patient-derived organoids recapitulate the basic features of primary tumors, including histological complexity and genetic heterogeneity, which is instrumental in predicting patient response to drugs. METHODS: Human iPSCs from healthy donors, breast and ovarian cancer patients were successfully differentiated towards isogenic hepatic, cardiac, neural and endothelial lineages. Multicellular organoids were established using Primary cells isolated from tumor tissues, histologically normal tissues adjacent to the tumors (NATs) and adipose tissues (source of Mesenchymal Stem Cells) from ovarian and breast cancer patients. Further these organoids were propagated and used for drug resistance/sensitivity studies. RESULTS: Ovarian and breast cancer patients' organoids showed heterogeneity in drug resistance and sensitivity. iPSCs-derived cardiomyocytes, hepatocytes and neurons showed donor-to-donor variability of chemotherapeutic drug sensitivity in ovarian cancer patients, breast cancer patients and healthy donors. CONCLUSION: We report development of a novel integrated platform to facilitate clinical decision-making using the patient's primary cells, iPSCs and derivatives, to clinically relevant models for oncology research.

Critical observation of WHO recommended multidrug therapy on the disease leprosy through mathematical study.

Leprosy is a skin disease and it is characterized by a disorder of the peripheral nervous system which occurs due to the infection of Schwann cells. In this research article, we have formulated a four-dimensional ODE-based mathematical model which consists of the densities of healthy Schwann cells, infected Schwann cells, M. leprae bacteria, and the concentration of multidrug therapy (MDT). This work primarily aims on exploring the dynamical changes and interrelations of the system cell populations during the disease progression. Also, evaluating a critical value of the drug efficacy rate of MDT remains our key focus in this article so that a safe drug dose regimen for leprosy can be framed more effectively and realistically. We have examined the stability scenario of different equilibria and the occurrence of Hopf-bifurcation for the densities of our system cell populations with respect to the drug efficacy rate of MDT to gain insight on the precise impact of the efficiency rate on both the infected Schwann cell and the bacterial populations. Also, a necessary transversality condition for the occurrence of the bifurcation has been established. Our analytical and numerical investigations in this research work precisely explores that the process of demyelination, nerve regeneration, and infection of the healthy Schwann cells are the three most crucial factors in the leprosy pathogenesis and to control the M. leprae-induced infection of Schwann cells successfully, a more flexible version of MDT regime with efficacy rate varying in the range η∈(0.025,0.059) for 100-120 days in PB cases and 300 days in MB cases obtained in this research article should be applied. All of our analytical outcomes have been verified through numerical simulations and compared with some existing clinical findings.

Risk Factors and Drug Efficacy for Severe Illness in Hemodialysis Patients Infected with the Omicron Variant of COVID-19.

INTRODUCTION: The Omicron variant of the novel coronavirus (COVID-19) has been spreading more rapidly and is more infectious, posing a higher risk of death and treatment difficulty for patients undergoing hemodialysis. This study aims to explore the severity rate and risk factors for hemodialysis patients infected with the Omicron variant and to conduct a preliminary analysis of the clinical efficacy of drugs. METHODS: Clinical and biochemical indicators of 219 hemodialysis patients infected with the Omicron variant were statistically analyzed. The patients were divided into two groups based on whether they were severely ill or not, and multiple regression analysis was conducted to determine the risk factors for severe illness. The severely ill patients were then grouped based on discharge or death, and the treatment drugs were included as influencing factors for multiple regression analysis to determine the risk factors and protective factors for death of severely ill patients, and drug efficacy analysis was conducted. RESULTS: Analysis showed that diabetes, low oxygen saturation, and high C-reactive protein (CRP) were independent risk factors for severe illness in hemodialysis patients infected with the Omicron variant. A history of diabetes and high C-reactive significantly increased the risk of severe illness in patients (aOR: 1.450; aOR: 1.011), while a high oxygen saturation level can reduce this risk (aOR: 0.871). In addition, respiratory distress was an independent risk factor for death in severely patients, significantly reducing the probability of discharge for patients (aOR: 0.152). The drugs thymalfasin and Tanreqing significantly increased the probability of discharge for patients (aOR: 1.472; aOR: 3.104), with the latter having a higher correlation, but with a relatively longer effective course. CONCLUSION: Hemodialysis patients infected with the Omicron variant of COVID-19 should pay special attention to their history of diabetes, CRP, and oxygen saturation levels, as well as respiratory distress symptoms, to reduce the risk of severe illness and death. In addition, thymalfasin and Tanreqing may be considered in treatment.

Shifting levels of peripheral inflammatory profiles as an indicator for comorbid multiple autoimmune diseases and bipolar disorder: a case report.

Autoimmune diseases (AID) cause inflammatory changes in the peripheral blood, which might be a predisposing factor for the development of comorbid bipolar disorder (BD). The levels of peripheral inflammatory indicators and cytokines may also serve as potential biomarkers for predicting BD susceptibility and the efficacy of antipsychotics in patients with AID. Herein, we present the case of a 43-year-old female who has suffered from AID for over 16 years and was recently diagnosed with "bipolar and related disorder due to another medical condition".

Mutation-oriented profiling of autoinhibitory kinase conformations predicts RAF inhibitor efficacies.

Kinase-targeted therapies have the potential to improve the survival of patients with cancer. However, the cancer-specific spectrum of kinase alterations exhibits distinct functional properties and requires mutation-oriented drug treatments. Besides post-translational modifications and diverse intermolecular interactions of kinases, it is the distinct disease mutation which reshapes full-length kinase conformations, affecting their activity. Oncokinase mutation profiles differ between cancer types, as it was shown for BRAF in melanoma and non-small-cell lung cancers. Here, we present the target-oriented application of a kinase conformation (KinCon) reporter platform for live-cell measurements of autoinhibitory kinase activity states. The bioluminescence-based KinCon biosensor allows the tracking of conformation dynamics of full-length kinases in intact cells and real time. We show that the most frequent BRAF cancer mutations affect kinase conformations and thus the engagement and efficacy of V600E-specific BRAF inhibitors (BRAFi). We illustrate that the patient mutation harboring KinCon reporters display differences in the effectiveness of the three clinically approved BRAFi vemurafenib, encorafenib, and dabrafenib and the preclinical paradox breaker PLX8394. We confirmed KinCon-based drug efficacy predictions for BRAF mutations other than V600E in proliferation assays using patient-derived lung cancer cell lines and by analyzing downstream kinase signaling. The systematic implementation of such conformation reporters will allow to accelerate the decision process for the mutation-oriented RAF-kinase cancer therapy. Moreover, we illustrate that the presented kinase reporter concept can be extended to other kinases which harbor patient mutations. Overall, KinCon profiling provides additional mechanistic insights into full-length kinase functions by reporting protein-protein interaction (PPI)-dependent, mutation-specific, and drug-driven changes of kinase activity conformations.

SARS-CoV-2 VOCs, Mutational diversity and clinical outcome: Are they modulating drug efficacy by altered binding strength?

The global COVID-19 pandemic continues due to emerging Severe Acute Respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOC). Here, we performed comprehensive analysis of in-house sequenced SARS-CoV-2 genome mutations dynamics in the patients infected with the VOCs - Delta and Omicron, within Recovered and Mortality patients. Statistical analysis highlighted significant mutations - T4685A, N4992N, and G5063S in RdRp; T19R in NTD spike; K444N and N532H in RBD spike, associated with Delta mortality. Mutations, T19I in NTD spike, Q493R and N440K in the RBD spike were significantly associated with Omicron mortality. We performed molecular docking for possible effect of significant mutations on the binding of Remdesivir. We found that Remdesivir showed less binding efficacy with the mutant Spike protein of both Delta and Omicron mortality compared to recovered patients. This indicates that mortality associated mutations could have a modulatory effect on drug binding which could be associated with disease outcome.