In Silico and In Vitro Identification of P-Glycoprotein Inhibitors from a Library of 375 Phytochemicals.

Cancer therapy with clinically established anticancer drugs is frequently hampered by the development of drug resistance of tumors and severe side effects in normal organs and tissues. The demand for powerful, but less toxic, drugs is high. Phytochemicals represent an important reservoir for drug development and frequently exert less toxicity than synthetic drugs. Bioinformatics can accelerate and simplify the highly complex, time-consuming, and expensive drug development process. Here, we analyzed 375 phytochemicals using virtual screenings, molecular docking, and in silico toxicity predictions. Based on these in silico studies, six candidate compounds were further investigated in vitro. Resazurin assays were performed to determine the growth-inhibitory effects towards wild-type CCRF-CEM leukemia cells and their multidrug-resistant, P-glycoprotein (P-gp)-overexpressing subline, CEM/ADR5000. Flow cytometry was used to measure the potential to measure P-gp-mediated doxorubicin transport. Bidwillon A, neobavaisoflavone, coptisine, and z-guggulsterone all showed growth-inhibitory effects and moderate P-gp inhibition, whereas miltirone and chamazulene strongly inhibited tumor cell growth and strongly increased intracellular doxorubicin uptake. Bidwillon A and miltirone were selected for molecular docking to wildtype and mutated P-gp forms in closed and open conformations. The P-gp homology models harbored clinically relevant mutations, i.e., six single missense mutations (F336Y, A718C, Q725A, F728A, M949C, Y953C), three double mutations (Y310A-F728A; F343C-V982C; Y953A-F978A), or one quadruple mutation (Y307C-F728A-Y953A-F978A). The mutants did not show major differences in binding energies compared to wildtypes. Closed P-gp forms generally showed higher binding affinities than open ones. Closed conformations might stabilize the binding, thereby leading to higher binding affinities, while open conformations may favor the release of compounds into the extracellular space. In conclusion, this study described the capability of selected phytochemicals to overcome multidrug resistance.

Pomiferin targets SERCA, mTOR, and P-gp to induce autophagic cell death in apoptosis-resistant cancer cells, and reverses the MDR phenotype in cisplatin-resistant tumors in vivo.

Drug resistance in cancer has been classified as innate resistance or acquired resistance, which were characterized by apoptotic defects and ABC transporters overexpression respectively. Therefore, to preclude or reverse these resistance mechanisms could be a promising strategy to improve chemotherapeutic outcomes. In this study, a natural product from Osage Orange, pomiferin, was identified as a novel autophagy activator that circumvents innate resistance by triggering autophagic cell death via SERCA inhibition and activation of the CaMKKß-AMPK-mTOR signaling cascade. In addition, pomiferin also directly inhibited the P-gp (MDR1/ABCB1) efflux and reversed acquired resistance by potentiating the accumulation and efficacy of the chemotherapeutic agent, cisplatin. In vivo study demonstrated that pomiferin triggered calcium-mediated tumor suppression and exhibited an anti-metastatic effect in the LLC-1 lung cancer-bearing mouse model. Moreover, as an adjuvant, pomiferin potentiated the anti-tumor effect of the chemotherapeutic agent, cisplatin, in RM-1 drug-resistant prostate cancer-bearing mouse model by specially attenuating ABCB1-mediated drug efflux, but not ABCC5, thereby promoting the accumulation of cisplatin in tumors. Collectively, pomiferin may serve as a novel effective agent for circumventing drug resistance in clinical applications.

A drug repurposing approach for individualized cancer therapy based on transcriptome sequencing and virtual drug screening.

RNA-sequencing has been proposed as a valuable technique to develop individualized therapy concepts for cancer patients based on their tumor-specific mutational profiles. Here, we aimed to identify drugs and inhibitors in an individualized therapy-based drug repurposing approach focusing on missense mutations for 35 biopsies of cancer patients. The missense mutations belonged to 9 categories (ABC transporter, apoptosis, angiogenesis, cell cycle, DNA damage, kinase, protease, transcription factor, tumor suppressor). The highest percentages of missense mutations were observed in transcription factor genes. The mutational profiles of all 35 tumors were subjected to hierarchical heatmap clustering. All 7 leukemia biopsies clustered together and were separated from solid tumors. Based on these individual mutation profiles, two strategies for the identification of possible drug candidates were applied: Firstly, virtual screening of FDA-approved drugs based on the protein structures carrying particular missense mutations. Secondly, we mined the Drug Gene Interaction (DGI) database (https://www.dgidb.org/) to identify approved or experimental inhibitors for missense mutated proteins in our dataset of 35 tumors. In conclusion, our approach based on virtual drug screening of FDA-approved drugs and DGI-based inhibitor selection may provide new, individual treatment options for patients with otherwise refractory tumors that do not respond anymore to standard chemotherapy.

In Silico and In Vitro Screening of 50 Curcumin Compounds as EGFR and NF-κB Inhibitors.

The improvement of cancer chemotherapy remains a major challenge, and thus new drugs are urgently required to develop new treatment regimes. Curcumin, a polyphenolic antioxidant derived from the rhizome of turmeric (Curcuma longa L.), has undergone extensive preclinical investigations and, thereby, displayed remarkable efficacy in vitro and in vivo against cancer and other disorders. However, pharmacological limitations of curcumin stimulated the synthesis of numerous novel curcumin analogs, which need to be evaluated for their therapeutic potential. In the present study, we calculated the binding affinities of 50 curcumin derivatives to known cancer-related target proteins of curcumin, i.e., epidermal growth factor receptor (EGFR) and nuclear factor κB (NF-κB) by using a molecular docking approach. The binding energies for EGFR were in a range of −12.12 (±0.21) to −7.34 (±0.07) kcal/mol and those for NF-κB ranged from −12.97 (±0.47) to −6.24 (±0.06) kcal/mol, indicating similar binding affinities of the curcumin compounds for both target proteins. The predicted receptor-ligand binding constants for EGFR and curcumin derivatives were in a range of 0.00013 (±0.00006) to 3.45 (±0.10) µM and for NF-κB in a range of 0.0004 (±0.0003) to 10.05 (±4.03) µM, indicating that the receptor-ligand binding was more stable for EGFR than for NF-κB. Twenty out of 50 curcumin compounds showed binding energies to NF-κB smaller than −10 kcal/mol, while curcumin as a lead compound revealed free binding energies of >−10 kcal/mol. Comparable data were obtained for EGFR: 15 out of 50 curcumin compounds were bound to EGFR with free binding energies of <−10 kcal/mol, while the binding affinity of curcumin itself was >−10 kcal/mol. This indicates that the derivatization of curcumin may indeed be a promising strategy to improve targe specificity and to obtain more effective anticancer drug candidates. The in silico results have been exemplarily validated using microscale thermophoresis. The bioactivity has been further investigated by using resazurin cell viability assay, lactate dehydrogenase assay, flow cytometric measurement of reactive oxygen species, and annexin V/propidium iodide assay. In conclusion, molecular docking represents a valuable approach to facilitate and speed up the identification of novel targeted curcumin-based drugs to treat cancer.

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 (-9.65 kcal/mol and 0.084 µM) were comparable or even better than those of the known WNK3 inhibitor PP-121 (-9.42 kcal/mol and 0.123 µM). 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.

Identification of Novel Anthracycline Resistance Genes and Their Inhibitors.

Differentially expressed genes have been previously identified by us in multidrug-resistant tumor cells mainly resistant to doxorubicin. In the present study, we exemplarily focused on some of these genes to investigate their causative relationship with drug resistance. HMOX1, NEIL2, and PRKCA were overexpressed by lentiviral-plasmid-based transfection of HEK293 cells. An in silico drug repurposing approach was applied using virtual screening and molecular docking of FDA-approved drugs to identify inhibitors of these new drug-resistant genes. Overexpression of the selected genes conferred resistance to doxorubicin and daunorubicin but not to vincristine, docetaxel, and cisplatin, indicating the involvement of these genes in resistance to anthracyclines but not to a broader MDR phenotype. Using virtual drug screening and molecular docking analyses, we identified FDA-approved compounds (conivaptan, bexarotene, and desloratadine) that were interacting with HMOX1 and PRKCA at even stronger binding affinities than 1-(adamantan-1-yl)-2-(1H-imidazol-1-yl)ethenone and ellagic acid as known inhibitors of HMOX1 and PRKCA, respectively. Conivaptan treatment increased doxorubicin sensitivity of both HMOX1- and PRKCA-transfected cell lines. Bexarotene treatment had a comparable doxorubicin-sensitizing effect in HMOX1-transfected cells and desloratadine in PRKCA-transfected cells. Novel drug resistance mechanisms independent of ABC transporters have been identified that contribute to anthracycline resistance in MDR cells.

Polyoxypregnanes as safe, potent, and specific ABCB1-inhibitory pro-drugs to overcome multidrug resistance in cancer chemotherapy in vitro and in vivo.

Multidrug resistance (MDR) mediated by ATP binding cassette subfamily B member 1 (ABCB1) is significantly hindering effective cancer chemotherapy. However, currently, no ABCB1-inhibitory drugs have been approved to treat MDR cancer clinically, mainly due to the inhibitor specificity, toxicity, and drug interactions. Here, we reported that three polyoxypregnanes (POPs) as the most abundant constituents of Marsdenia tenacissima (M. tenacissima) were novel ABCB1-modulatory pro-drugs, which underwent intestinal microbiota-mediated biotransformation in vivo to generate active metabolites. The metabolites at non-toxic concentrations restored chemosensitivity in ABCB1-overexpressing cancer cells via inhibiting ABCB1 efflux activity without changing ABCB1 protein expression, which were further identified as specific non-competitive inhibitors of ABCB1 showing multiple binding sites within ABCB1 drug cavity. These POPs did not exhibit ABCB1/drug metabolizing enzymes interplay, and their repeated administration generated predictable pharmacokinetic interaction with paclitaxel without obvious toxicity in vivo. We further showed that these POPs enhanced the accumulation of paclitaxel in tumors and overcame ABCB1-mediated chemoresistance. The results suggested that these POPs had the potential to be developed as safe, potent, and specific pro-drugs to reverse ABCB1-mediated MDR. Our work also provided scientific evidence for the use of M. tenacissima in combinational chemotherapy.

Synthesis, computational docking and biological evaluation of celastrol derivatives as dual inhibitors of SERCA and P-glycoprotein in cancer therapy.

A series of eleven celastrol derivatives was designed, synthesized, and evaluated for their in vitro cytotoxic activities against six human cancer cell lines (A549, HepG2, HepAD38, PC3, DLD-1 Bax-Bak WT and DKO) and three human normal cells (LO2, BEAS-2B, CCD19Lu). To our knowledge, six derivatives were the first example of dipeptide celastrol derivatives. Among them, compound 3 was the most promising derivative, as it exhibited a remarkable anti-proliferative activity and improved selectivity in liver cancer HepAD38 versus human normal hepatocytes, LO2. Compound 6 showed higher selectivity in liver cancer cells against human normal lung fibroblasts, CCD19Lu cell line. The Ca2+ mobilizations of 3 and 6 were also evaluated in the presence and absence of thapsigargin to demonstrate their inhibitory effects on SERCA. Derivatives 3 and 6 were found to induce apoptosis on LO2, HepG2 and HepAD38 cells. The potential docking poses of all synthesized celastrol dipeptides and other known inhibitors were proposed by molecular docking. Finally, 3 inhibited P-gp-mediated drug efflux with greater efficiency than inhibitor verapamil in A549 lung cancer cells. Therefore, celastrol-dipeptide derivatives are potent drug candidates for the treatment of drug-resistant cancer.

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 drug resistance mechanisms by genomic and transcriptomic profiling of glioblastoma cells with mutation-activated EGFR.

AIMS: Epidermal growth factor receptor (EGFR) is not only involved in carcinogenesis, but also in chemoresistance. We characterized U87.MGΔEGFR glioblastoma cells with constitutively active EGFR due to deletion at the ligand binding domain in terms of gene expression profiling and chromosomal aberrations. Wild-type U87.MG cells served as control. MATERIALS AND METHODS: RNA sequencing and network analyses (Ingenuity Pathway Analysis) were performed to identify novel drug resistance mechanisms related to expression of mutation activated EGFR. Chromosomal aberrations were characterized by multicolor fluorescence in situ hybridization (mFISH) and array comparative genomic hybridization (aCGH). KEY FINDINGS: U87.MGΔEGFR cells presented much more chromosomal aberrations, amplifications and deletions than wild-type U87.MG cells. Both cell lines were near-triploid. Numerous genes were overexpressed in U87.MGΔEGFR cells, some of which have been already linked to drug resistance. PXDN, which is associated with epithelial mesenchymal transition, was the most upregulated gene (901.8-fold). TENM1 was 331.6-fold upregulated, and it was previously reported to modulate neural development. EGFR-AS1 (161.2-fold upregulated) has been reported to increase the EGFR mRNA stability and its expression - in accordance with that of EGFR - was upregulated (85.5-fold). In addition to well-known resistance genes, numerous novel genes and genomic aberrations were identified. ANGPT2 upregulation and CPM downregulation were validated by Western blotting. SIGNIFICANCE: Transcriptomics and genomics analyses in U87.MGΔEGFR cells unraveled a range of novel drug resistance mechanisms including apoptosis, DNA repair, ferroptosis, glutathione related gene activities, heat shock, oxidative stress, transcription factor activities, which may have important implications for future treatment strategies.

Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning.

Coronavirus disease 2019 (COVID-19) is a major threat worldwide due to its fast spreading. As yet, there are no established drugs available. Speeding up drug discovery is urgently required. We applied a workflow of combined in silico methods (virtual drug screening, molecular docking and supervised machine learning algorithms) to identify novel drug candidates against COVID-19. We constructed chemical libraries consisting of FDA-approved drugs for drug repositioning and of natural compound datasets from literature mining and the ZINC database to select compounds interacting with SARS-CoV-2 target proteins (spike protein, nucleocapsid protein, and 2'-o-ribose methyltransferase). Supported by the supercomputer MOGON, candidate compounds were predicted as presumable SARS-CoV-2 inhibitors. Interestingly, several approved drugs against hepatitis C virus (HCV), another enveloped (-) ssRNA virus (paritaprevir, simeprevir and velpatasvir) as well as drugs against transmissible diseases, against cancer, or other diseases were identified as candidates against SARS-CoV-2. This result is supported by reports that anti-HCV compounds are also active against Middle East Respiratory Virus Syndrome (MERS) coronavirus. The candidate compounds identified by us may help to speed up the drug development against SARS-CoV-2.

Identification of potential novel drug resistance mechanisms by genomic and transcriptomic profiling of colon cancer cells with p53 deletion.

TP53 (p53) is a pivotal player in tumor suppression with fifty percent of all invasive tumors displaying mutations in the TP53 gene. In the present study, we characterized colon cancer cells (HCT116 p53 -/-) with TP53 deletion, a sub-line derived from HCT116-p53 +/+ cells. RNA sequencing and network analyses were performed to identify novel drug resistance mechanisms. Chromosomal aberrations were identified by multicolor fluorescence in situ hybridization (mFISH) and array comparative genomic hybridization (aCGH). Numerous genes were overexpressed in HCT116 p53 -/- cells: RND3/RhoE (235.6-fold up-regulated), DCLK1 (60.2-fold up-regulated), LBH (31.9-fold up-regulated), MYB (28.9-fold up-regulated), TACSTD2 (110.1-fold down-regulated), NRIP1 (81.5-fold down-regulated) and HLA-DMB (69.7-fold down-regulated) are among the identified genes with potential influence on multidrug resistance (MDR) and they are associated with cancer progression and tumorigenesis, according to previously published studies. Probably due to TP53 deletion, disturbances in DNA repair and apoptosis are leading to aberrancies in cellular and organismal organization, ultimately increasing tumorigenesis and cancer progression potential. With NFκB, PI3K and HSP70, being at the center of merged protein network, and TH1-2 pathways, being among the influenced pathways, it can be speculated that the inflammatory pathway contributes to a resistance phenotype together with cell cycle regulation and heat-shock response. HCT116-p53 -/- cells have more chromosomal aberrations, gains and losses in copy numbers than HCT116-p53 +/+ cells. In conclusion, numerous genomic aberrations, which might be associated with yet unknown drug resistance mechanisms, were identified. This may have important implications for future treatment strategies.

Drug repurposing using transcriptome sequencing and virtual drug screening in a patient with glioblastoma.

Background Precision medicine and drug repurposing are attractive strategies, especially for tumors with worse prognosis. Glioblastoma is a highly malignant brain tumor with limited treatment options and short survival times. We identified novel BRAF (47-438del) and PIK3R1 (G376R) mutations in a glioblastoma patient by RNA-sequencing. Methods The protein expression of BRAF and PIK3R1 as well as the lack of EGFR expression as analyzed by immunohistochemistry corroborated RNA-sequencing data. The expression of additional markers (AKT, SRC, mTOR, NF-κB, Ki-67) emphasized the aggressiveness of the tumor. Then, we screened a chemical library of > 1500 FDA-approved drugs and > 25,000 novel compounds in the ZINC database to find established drugs targeting BRAF47-438del and PIK3R1-G376R mutated proteins. Results Several compounds (including anthracyclines) bound with higher affinities than the control drugs (sorafenib and vemurafenib for BRAF and PI-103 and LY-294,002 for PIK3R1). Subsequent cytotoxicity analyses showed that anthracyclines might be suitable drug candidates. Aclarubicin revealed higher cytotoxicity than both sorafenib and vemurafenib, whereas idarubicin and daunorubicin revealed higher cytotoxicity than LY-294,002. Liposomal formulations of anthracyclines may be suitable to cross the blood brain barrier. Conclusions In conclusion, we identified novel small molecules via a drug repurposing approach that could be effectively used for personalized glioblastoma therapy especially for patients carrying BRAF47-438del and PIK3R1-G376R mutations.

Identification of metastasis-related genes by genomic and transcriptomic studies in murine melanoma.

AIMS: We systematically characterized metastatic murine B16-F10 melanoma, a sub-line derived from murine melanoma B16-F1 cells. MATERIALS AND METHODS: RNA-sequencing and network analyses (Ingenuity Pathway Analysis) were performed to identify novel potential metastasis mechanisms. Chromosomal aberrations were identified by multicolor fluorescence in situ hybridization (mFISH) using all 21 murine whole chromosome painting probes. KEY FINDINGS: Numerous genes were overexpressed in B16-F10 cells, some of which have been already described as being metastasis-linked. Nr5a1/sf1, a known prognostic marker for adrenal tumors, was 177-fold upregulated in B16-F10 cells compared to B16-F1 cells. Hoxb8 was 75-fold upregulated, which was previously associated with gastric cancer progression and metastasis. Ptk7, which is linked with tumorigenesis and metastasis of esophageal squamous carcinoma, was 67-fold upregulated. B16-F10 cells acquired additional chromosomal aberrations compared to B16-F1 cells, including dic(4)(pter->qter:qter->pter), +dic(6;15), +der(10)t(10;?1;16). SIGNIFICANCE: In addition to well-known metastatic genes, numerous novel genes and genomic aberrations were identified, which may serve as targets for treatment in the future. Transcriptomic and genetic analyses in B16-F10 cells unraveled a range of novel metastasis mechanisms, which may also have important implications for future treatment strategies.

Isopetasin and S-isopetasin as novel P-glycoprotein inhibitors against multidrug-resistant cancer cells.

BACKGROUND: A major problem of cancer treatment is the development of multidrug resistance (MDR) to chemotherapy. MDR is caused by different mechanisms such as the expression of the ABC-transporters P-glycoprotein (P-gp, MDR1, ABCB1) and breast cancer resistance protein (BCRP, ABCG2). These transporters efflux xenobiotic toxins, including chemotherapeutics, and they were found to be overexpressed in different cancer types. PURPOSE: Identification of novel molecules that overcome MDR by targeting ABC-transporters. METHODS: Resazurin reduction assay was used for cytotoxicity test. AutoDock 4.2. was used for molecular docking. The function of P-gp and BCRP was tested using a doxorubicin uptake assay and an ATPase assay. ROS generation was detected using flow cytometry for the measurement of H2DCFH-DA fluorescence. Annexin/PI staining was applied for the detection of apoptosis. Bioinformatic analyses were performed using LigandScout 3.12. software and DataWarrior software. RESULTS: In our search for new molecules that selectively act against resistant phenotypes, we identified isopetasin and S-isopetasin, which are bioactive natural products from Petasites formosanus. They exerted collateral sensitivity towards leukemia cells with high P-gp expression in CEM/ADR5000 cells, compared to sensitive wild-type CCRF-CEM leukemia cells. Also, they revealed considerable activity towards breast cancer cells overexpressing breast cancer resistance protein, MDA-MB-231-BCRP clone 23. This motivated us to investigate whether the function of P-gp was inhibited. In-silico results showed the compounds bound with high affinity and interacted with key amino acid residues in P-gp . Then, we found that the two compounds increased doxorubicin accumulation in P-gp overexpressing CEM/ADR5000 by three-fold compared to cells without inhibitor. P-gp-mediated drug efflux was ATP-dependent. Isopetasin and S-isopetasin increased the ATPase activity of human P-gp in a comparable fashion as verapamil used as control P-gp inhibitor. As isopetasin and S-isopetasin exerted dual roles, first as cytotoxic compounds and then as P-gp inhibitors, we suggested that their P-gp inhibition is part of a larger complex of mechanisms to induce cell death in cancer patients. P-gp dysfunction induces mitochondrial stress to generate ATP. Upon continuing stress by P-gp inhibition, the mitochondria generate reactive oxygen species (ROS). Initially established for verapamil, this theory was validated in the present study for isopetasin and S-isopetasin, as treatment with the two candidates increased ROS levels in CEM/ADR5000 cells followed by apoptosis. CONCLUSION: Our study highlights the importance of isopetasin and S-isopetasin as novel ROS-generating and apoptosis-inducing P-gp inhibitors.

Effect of ABC transporter expression and mutational status on survival rates of cancer patients.

ATP-binding cassette (ABC) transporters mediate multidrug resistance in cancer. In contrast to DNA single nucleotide polymorphisms in normal tissues, the role of mutations in tumors is unknown. Furthermore, the significance of their expression for prediction of chemoresistance and survival prognosis is still under debate. We investigated 18 tumors by RNA-sequencing. The mutation rate varied from 27,507 to 300885. In ABCB1, three hotspots with novel mutations were in transmembrane domains 3, 8, and 9. We also mined the cBioPortal database with 11,814 patients from 23 different tumor entities. We performed Kaplan-Meier survival analyses to investigate the effect of ABC transporter expression on survival rates of cancer patients. Novel mutations were also found in ABCA2, ABCA3, ABCB2, ABCB5, ABCC1-6, and ABCG2. Mining the cBioPortal database with 11,814 patients from 23 different tumor entities validated our results. Missense and in-frame mutations led to altered binding of anticancer drugs in molecular docking approaches. The ABCB1 nonsense mutation Q856* led to a truncated P-glycoprotein, which may sensitize tumors to anticancer drugs. The search for ABC transporter nonsense mutations represents a novel approach for precision medicine.. Low ABCB1 mRNA expression correlated with significantly longer survival in ovarian or kidney cancer and thymoma. In cancers of breast, kidney or lung, ABC transporter expression correlated with different tumor stages and human populations as further parameters to refine strategies for more individualized chemotherapy.

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.

Collateral sensitivity of natural products in drug-resistant cancer cells.

Cancer chemotherapy is frequently hampered by drug resistance. Concepts to combine anticancer drugs with different modes of action to avoid the development of resistance did not provide the expected success in the past, because tumors can be simultaneously non-responsive to many drugs (e.g. the multidrug resistance phenotype). However, tumors may be specifically hypersensitive to other drugs - a phenomenon also termed collateral sensitivity. This seems to be a general biological mechanism, since it also occurs in drug-resistant Escherichia coli and Saccharomyces cerevisiae. Here, we give a timely and comprehensive overview on hypersensitivity in resistant cancer cells towards natural products and their derivatives. Since the majority of clinically established anticancer drugs are natural products or are in one way or another derived from them, it is worth hypothesizing that natural products may deliver promising lead compounds for the development of collateral sensitive anticancer drugs. Hypersensitivity occurs not only in classical ABC transporter-mediated multidrug resistance, but also in many other resistance phenotypes. Resistant cancers can be hypersensitive to natural compounds from diverse classes and origins (i.e. mitotic spindle poisons, DNA topoisomerase 1 and 2 inhibitors, diverse phytochemicals isolated from medicinal plants, (semi)synthetic derivatives of phytochemicals, antibiotics, marine drugs, recombinant therapeutic proteins and others). Molecular mechanisms of collateral sensitivity include (1) increased ATP hydrolysis and reactive oxygen species production by futile cycling during ABC transporter-mediated drug efflux, (2) inhibition of ATP production, and (3) alterations of drug target proteins (e.g. increased expression of topoisomerases and heat shock proteins, inhibition of Wnt/ß-catenin pathway, mutations in ß-tubulin). The phenomenon of hypersensitivity needs to be exploited for clinical oncology by the development of (1) novel combination protocols that include collateral sensitive drugs and (2) novel drugs that specifically exhibit high degrees of hypersensitivity in resistant tumors.

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.