Biopiracy <i>versus </i>one-world medicine – from colonial relicts to global collaborative concepts

Background: Practices of biopiracy to use genetic resources and indigenous knowledge by Western companies without benefit-sharing of those, who generated the traditional knowledge, can be understood as form of neocolonialism.Hypothesis: : The One-World Medicine concept attempts to merge the best of traditional medicine from developing countries and conventional Western medicine for the sake of patients around the globe.Study design: Based on literature searches in several databases, a concept paper has been written. Legislative initiatives of the United Nations culminated in the Nagoya protocol aim to protect traditional knowledge and regulate benefit-sharing with indigenous communities. The European community adopted the Nagoya protocol, and the corresponding regulations will be implemented into national legislation among the member states. Despite pleasing progress, infrastructural problems of the health care systems in developing countries still remain. Current approaches to secure primary health care offer only fragmentary solutions at best. Conventional medicine from industrialized countries cannot be afforded by the impoverished population in the Third World. Confronted with exploding costs, even health systems in Western countries are endangered to burst. Complementary and alternative medicine (CAM) is popular among the general public in industrialized countries, although the efficacy is not sufficiently proven according to the standards of evidence-based medicine. CAM is often available without prescription as over-the-counter products with non-calculated risks concerning erroneous self-medication and safety/toxicity issues. The concept of integrative medicine attempts to combine holistic CAM approaches with evidence-based principles of conventional medicine.Conclusion: To realize the concept of One-World Medicine, a number of standards have to be set to assure safety, efficacy and applicability of traditional medicine, e.g. sustainable production and quality control of herbal products, performance of placebo-controlled, double-blind, randomized clinical trials, phytovigilance, as well as education of health professionals and patients

An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors

The Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). The virus has rapidly spread in humans, causing the ongoing Coronavirus pandemic. Recent studies have shown that, similarly to SARS-CoV, SARS-CoV-2 utilises the Spike glycoprotein on the envelope to recognise and bind the human receptor ACE2. This event initiates the fusion of viral and host cell membranes and then the viral entry into the host cell. Despite several ongoing clinical studies, there are currently no approved vaccines or drugs that specifically target SARS-CoV-2. Until an effective vaccine is available, repurposing FDA approved drugs could significantly shorten the time and reduce the cost compared to de novo drug discovery. In this study we attempted to overcome the limitation of in silico virtual screening by applying a robust in silico drug repurposing strategy. We combined and integrated docking simulations, with molecular dynamics (MD), Supervised MD (SuMD) and Steered MD (SMD) simulations to identify a Spike protein – ACE2 interaction inhibitor. Our data showed that Simeprevir and Lumacaftor bind the receptor-binding domain of the Spike protein with high affinity and prevent ACE2 interaction

A Machine Learning-Based Prediction Platform for P-Glycoprotein Modulators and Its Validation by Molecular Docking

P-glycoprotein (P-gp) is an important determinant of multidrug resistance (MDR) because its overexpression is associated with increased efflux of various established chemotherapy drugs in many clinically resistant and refractory tumors. This leads to insufficient therapeutic targeting of tumor populations, representing a major drawback of cancer chemotherapy. Therefore, P-gp is a target for pharmacological inhibitors to overcome MDR. In the present study, we utilized machine learning strategies to establish a model for P-gp modulators to predict whether a given compound would behave as substrate or inhibitor of P-gp. Random forest feature selection algorithm-based leave-one-out random sampling was used. Testing the model with an external validation set revealed high performance scores. A P-gp modulator list of compounds from the ChEMBL database was used to test the performance, and predictions from both substrate and inhibitor classes were selected for the last step of validation with molecular docking. Predicted substrates revealed similar docking poses than that of doxorubicin, and predicted inhibitors revealed similar docking poses than that of the known P-gp inhibitor elacridar, implying the validity of the predictions. We conclude that the machine-learning approach introduced in this investigation may serve as a tool for the rapid detection of P-gp substrates and inhibitors in large chemical libraries

Pharmacogenomic Characterization of Cytotoxic Compounds from <i>Salvia officinalis</i> in Cancer Cells

<i>Salvia officinalis</i> is used as a dietary supplement with diverse medicinal activity (e.g. antidiabetic and antiatherosclerotic effects). The plant also exerts profound cytotoxicity toward cancer cells. Here, we investigated possible modes of action to explain its activity toward drug-resistant tumor cells. Log₁₀IC₅₀ values of two constituents of <i>S. officinalis</i> (ursolic acid, pomolic acid) were correlated to the expression of ATP-binding cassette (ABC) transporters (P-glycoprotein/<i>ABCB1/MDR1</i>, MRP1/<i>ABCC1</i>, BCRP/<i>ABCG2</i>) and epidermal growth factor receptor (<i>EGFR</i>) or mutations in <i>RAS</i> oncogenes and the tumor suppressor gene <i>TP53</i> of the NCI panel of cell lines. Gene expression profiles predicting sensitivity and resistance of tumor cells to these compounds were determined by microarray-based mRNA expressions, COMPARE, and hierarchical cluster analyses. Furthermore, the binding of both plant acids to key molecules of the NF-κB pathway (NF-κB, I-κB, NEMO) was analyzed by molecular docking. Neither expression nor mutation of ABC transporters, oncogenes, or tumor suppressor genes correlated with log₁₀IC₅₀ values for ursolic acid or pomolic acid. In microarray analyses, many genes involved in signal transduction processes correlated with cellular responsiveness to these compounds. Molecular docking indicated that the two plant acids strongly bound to target proteins of the NF-κB pathway with even lower free binding energies than the known NF-κB inhibitor MG-132. They interacted more strongly with DNA-bound NF-κB than free NF-κB, pointing to inhibition of DNA binding by these compounds. In conclusion, the lack of cross-resistance to classical drug resistance mechanisms (ABC-transporters, oncogenes, tumor suppressors) may indicate a promising role of the both plant acids for cancer chemotherapy. Genes involved in signal transduction may contribute to the sensitivity or resistance of tumor cells to ursolic and pomolic acids. Ursolic and pomolic acid may target different steps of the NF-κB pathway to inhibit NF-κB-mediated functions

Kinome-Wide Profiling Identifies Human WNK3 as a Target of Cajanin Stilbene Acid from Cajanus cajan (L.) Millsp.

Pigeon Pea (Cajanus cajan (L.) Millsp.) is a common food crop used in many parts of the world for nutritional purposes. One of its chemical constituents is cajanin stilbene acid (CSA), which exerts anticancer activity in vitro and in vivo. In an effort to identify molecular targets of CSA, we performed a kinome-wide approach based on the measurement of the enzymatic activities of 252 human kinases. The serine-threonine kinase WNK3 (also known as protein kinase lysine-deficient 3) was identified as the most promising target of CSA with the strongest enzymatic activity inhibition in vitro and the highest binding affinity in molecular docking in silico. The lowest binding affinity and the predicted binding constant pKi of CSA (&minus;9.65 kcal/mol and 0.084 &micro;M) were comparable or even better than those of the known WNK3 inhibitor PP-121 (&minus;9.42 kcal/mol and 0.123 &micro;M). The statistically significant association between WNK3 mRNA expression and cellular responsiveness to several clinically established anticancer drugs in a panel of 60 tumor cell lines and the prognostic value of WNK3 mRNA expression in sarcoma biopsies for the survival time of 230 patients can be taken as clues that CSA-based inhibition of WNK3 may improve treatment outcomes of cancer patients and that CSA may serve as a valuable supplement to the currently used combination therapy protocols in oncology

Synergy and Antagonism of Active Constituents of ADAPT-232 on Transcriptional Level of Metabolic Regulation of Isolated Neuroglial Cells

Gene expression profiling was performed on the human neuroglial cell line T98G after treatment with adaptogen ADAPT-232 and its constituents – extracts of Eleutherococcus senticosus root, Schisandra chinensis berry, and Rhodiola rosea root as well as several constituents individually, namely, eleutheroside E, schizandrin B, salidroside, triandrin, and tyrosol. A common feature for all tested adaptogens was their effect on G-protein-coupled receptor (GPCR) signaling pathways, i.e. cAMP, phospholipase C and phosphatidylinositol signal transduction pathways. Adaptogens may reduce the cAMP level in brain cells by downregulation of adenylate cyclase gene ADC2Y and upregulation of phosphodiestherase gene PDE4D that is essential for energy homeostasis as well as for switching from catabolic to anabolic states and vice versa. All tested adaptogens up-regulated the PLCB1 gene, which encodes phosphoinositide-specific phospholipase C (PLC) and phosphatidylinositol 3-kinases (PI3Ks), key players for the regulation of NF-B-mediated defense responses. Other common targets of adaptogens included genes encoding ERα estrogen receptor(2.9-22.6 fold down-regulation), cholesterol ester transfer protein (5.1-10.6 fold down-regulation), heat shock protein Hsp70 (3.0-45.0 fold up-regulation), serpin peptidase inhibitor (neuroserpin), and 5-HT3 receptor of serotonin (2.2-6.6 fold down-regulation). These findings can be reconciled with the observed beneficial effects of adaptogens in behavioral, mental and aging-associated disorders. Combining two or more active substances in one mixture significantly changes deregulated genes profiles: synergetic interactions result in activation of genes that none of the individual substances affected, while antagonistic interactions result in suppression some genes activated by individual substances. Merging of deregulated genes array profiles and intracellular networks is specific to the new substance with unique pharmacological characteristics

Selection of safe artemisinin derivatives using a machine learning-based cardiotoxicity platform and in vitro and in vivo validation

The majority of drug candidates fails the approval phase due to unwanted toxicities and side effects. Establishment of an effective toxicity prediction platform is of utmost importance, to increase the efficiency of the drug discovery process. For this purpose, we developed a toxicity prediction platform with machine-learning strategies. Cardiotoxicity prediction was performed by establishing a model with five parameters (arrhythmia, cardiac failure, heart block, hypertension, myocardial infarction) and additional toxicity predictions such as hepatotoxicity, reproductive toxicity, mutagenicity, and tumorigenicity are performed by using Data Warrior and Pro-Tox-II software. As a case study, we selected artemisinin derivatives to evaluate the platform and to provide a list of safe artemisinin derivatives. Artemisinin from Artemisia annua was described first as an anti-malarial compound and later its anticancer properties were discovered. Here, random forest feature selection algorithm was used for the establishment of cardiotoxicity models. High AUC scores above 0.830 were achieved for all five cardiotoxicity indications. Using a chemical library of 374 artemisinin derivatives as a case study, 7 compounds (deoxydihydro-artemisinin, 3-hydroxy-deoxy-dihydroartemisinin, 3-desoxy-dihydroartemisinin, dihydroartemisinin-furano acetate-d3, deoxyartemisinin, artemisinin G, artemisinin B) passed the toxicity filtering process for hepatotoxicity, mutagenicity, tumorigenicity, and reproductive toxicity in addition to cardiotoxicity. Experimental validation with the cardiomyocyte cell line AC16 supported the findings from the in silico cardiotoxicity model predictions. Transcriptomic profiling of AC16 cells upon artemisinin B treatment revealed a similar gene expression profile as that of the control compound, dexrazoxane. In vivo experiments with a Zebrafish model further substantiated the in silico and in vitro data, as only slight cardiotoxicity in picomolar range was observed. In conclusion, our machine-learning approach combined with in vitro and in vivo experimentation represents a suitable method to predict cardiotoxicity of drug candidates

Identification of Novel Rare ABCC1 Transporter Mutations in Tumor Biopsies of Cancer Patients

The efficiency of chemotherapy drugs can be affected by ATP-binding cassette (ABC) transporter expression or by their mutation status. Multidrug resistance is linked with ABC transporter overexpression. In the present study, we performed rare mutation analyses for 12 ABC transporters related to drug resistance (ABCA2, -A3, -B1, -B2, -B5, -C1, -C2, -C3, -C4, -C5, -C6, -G2) in a dataset of 18 cancer patients. We focused on rare mutations resembling tumor heterogeneity of ABC transporters in small tumor subpopulations. Novel rare mutations were found in ABCC1, but not in the other ABC transporters investigated. Diverse ABCC1 mutations were found, including nonsense mutations causing premature stop codons, and compared with the wild-type protein in terms of their protein structure. Nonsense mutations lead to truncated protein structures. Molecular docking and heat map analyses of ABCC1/MRP1 pointed out that Lys498* appeared in a separate cluster branch due to the large deletion, leading to a massive disruption in the protein conformation. The resulting proteins, which are nonfunctional due to nonsense mutations in tumors, offer a promising chemotherapy strategy since tumors with nonsense mutations may be more sensitive to anticancer drugs than wild-type ABCC1-expressing tumors. This could provide a novel tumor-specific toxicity strategy and a way to overcome drug resistance