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Sooda K, Allison SJ, Javid FA. Investigation of the cytotoxicity induced by cannabinoids on human ovarian carcinoma cells. Pharmacol Res Perspect 2023; 11:e01152. [PMID: 38100640 PMCID: PMC10723784 DOI: 10.1002/prp2.1152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 09/22/2023] [Accepted: 10/12/2023] [Indexed: 12/17/2023] Open
Abstract
Cannabinoids have been shown to induce anti-tumor activity in a variety of carcinoma cells such as breast, prostate, and brain. The aim of the present study is to investigate the anti-tumor activity of cannabinoids, CBD (cannbidiol), and CBG (cannabigerol) in ovarian carcinoma cells sensitive and resistant to chemotherapeutic drugs. Sensitive A2780 cells and resistant A2780/CP70 carcinoma cells and non-carcinoma cells were exposed to varying concentrations of CBD, CBG, carboplatin or CB1 and CB2 receptor antagonists, AM251 and AM630, respectively, alone or in combination, at different exposure times and cytotoxicity was measured by MTT assay. The mechanism of action of CBD and CB in inducing cytotoxicity was investigated involving a variety of apoptotic and cell cycle assays. Treatment with CBD and CBG selectively, dose and time dependently reduced cell viability and induced apoptosis. The effect of CBD was stronger than CBG in all cell lines tested. Both CBD and CBG induced stronger cytotoxicity than afforded by carboplatin in resistant cells. The cytotoxicity induced by CBD was not CB1 or CB2 receptor dependent in both carcinoma cells, however, CBG-induced cytotoxicity may involve CB1 receptor activity in cisplatin-resistant carcinoma cells. A synergistic effect was observed when cannabinoids at sublethal doses were combined with carboplatin in both carcinoma cells. The apoptotic event may involve loss of mitochondrial membrane potential, Annexin V, caspase 3/7, ROS activities, and cell cycle arrest. Further studies are required to investigate whether these results are translatable in the clinic. Combination therapies with conventional cancer treatments using cannabinoids are suggested.
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Affiliation(s)
- Kartheek Sooda
- Department of Pharmacy, School of Applied SciencesUniversity of HuddersfieldHuddersfieldUK
| | - Simon J. Allison
- Department of Biological & Geographical Sciences, School of Applied SciencesUniversity of HuddersfieldHuddersfieldUK
| | - Farideh A. Javid
- Department of Pharmacy, School of Applied SciencesUniversity of HuddersfieldHuddersfieldUK
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2
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Bertin P, Rector-Brooks J, Sharma D, Gaudelet T, Anighoro A, Gross T, Martínez-Peña F, Tang EL, Suraj MS, Regep C, Hayter JBR, Korablyov M, Valiante N, van der Sloot A, Tyers M, Roberts CES, Bronstein MM, Lairson LL, Taylor-King JP, Bengio Y. RECOVER identifies synergistic drug combinations in vitro through sequential model optimization. CELL REPORTS METHODS 2023; 3:100599. [PMID: 37797618 PMCID: PMC10626197 DOI: 10.1016/j.crmeth.2023.100599] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 08/30/2023] [Accepted: 09/06/2023] [Indexed: 10/07/2023]
Abstract
For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges. Deep learning models have achieved state-of-the-art results in silico for the prediction of synergy scores. However, databases of drug combinations are biased toward synergistic agents and results do not generalize out of distribution. During 5 rounds of experimentation, we employ sequential model optimization with a deep learning model to select drug combinations increasingly enriched for synergism and active against a cancer cell line-evaluating only ∼5% of the total search space. Moreover, we find that learned drug embeddings (using structural information) begin to reflect biological mechanisms. In silico benchmarking suggests search queries are ∼5-10× enriched for highly synergistic drug combinations by using sequential rounds of evaluation when compared with random selection or ∼3× when using a pretrained model.
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Affiliation(s)
- Paul Bertin
- Mila, the Quebec AI Institute, Montreal, QC, Canada
| | | | | | | | | | | | | | - Eileen L Tang
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA, USA
| | | | | | | | | | | | - Almer van der Sloot
- IRIC, Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, QC, Canada
| | - Mike Tyers
- Program in Molecular Medicine, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, 686 Bay Street, Toronto, ON M5G 0A4, Canada
| | | | - Michael M Bronstein
- Relation Therapeutics, London, UK; Department of Computer Science, University of Oxford, Oxford, UK
| | - Luke L Lairson
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA, USA
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3
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Maurya L, Singh S, Shah K, Dewangan HK. Dual Vinorelbine bitartrate and Resveratrol Loaded Polymeric Aqueous core Nanocapsules for Synergistic Efficacy in Breast Cancer. J Microencapsul 2022; 39:299-313. [PMID: 35470755 DOI: 10.1080/02652048.2022.2070679] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
AIM The current study focused on the development and evaluation of aqueous core nanocapsules (ACNs) as an effective carrier to deliver an optimal synergistic combination of a highly water soluble Vinorelbine bitartrate (VRL) and a poorly water-soluble Resveratrol (RES) for treatment of breast cancer. METHODS Various molar ratios of VRL to RES were screened against MCF-7 cell lines to determine the synergistic effects using Chou-Talalay method. Synergistic ratio of therapeutic agents was then incorporated into aqueous core nanocapsules utilizing a double emulsion solvent evaporation technique to yield dual drug loaded nanocapsules (dd-ACNs). The dd-ACNs were optimized using Box-Behnken design and characterized for physicochemical parameters such as particle size, zeta potential, polydispersity index, total drug content and encapsulation efficiency, surface morphology, drug excipient compatibility by FTIR and DSC, release kinetics, toxicity studies and anticancer efficacy (in-vitro and in-vivo). RESULTS Results demonstrated that the combination exhibited maximum synergy when higher doses of VRL were combined with smaller doses of RES (1:1, 5:1, and 10:1). The dual drug loaded ACNs were found to be stable and depicted a core-shell structure, narrow size range (150.2 ± 3.2 nm) with enhanced encapsulation (80% for VRL and 99% for RES). Moreover, the dd-ACNs were 5 times more efficacious in-vitro than a combination of free drugs, while reducing systemic toxicity. Also, pre-clinical evaluation of dd-ACNs also depicted drastic reduction of tumor volume as compared tp pristine VRL and physical combination of drugs. CONCLUSION The developed dd-ACNs can be applied as potential carrier for delivery of combination of chemotherapeutics at a synergistic ratio at tumor site.
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Affiliation(s)
- Lakshmi Maurya
- KIET School of Pharmacy, KIET Group of Institutions, Delhi-NCR, Meerut Road (NH-58), Ghaziabad-201206, India
| | - Sanjay Singh
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi- 221005, India
| | - Kamal Shah
- Institute of Pharmaceutical Research (IPR), GLA University, Mathura, NH-2 Mathura Delhi Road, PO- Chamuhan, Mathura, Uttar Pradesh-281406, India
| | - Hitesh Kumar Dewangan
- University Institute of Pharma Sciences (UIPS), Chandigarh University NH-95, Chandigarh Ludhiana Highway, Mohali- 160101, Punjab, India
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Nafshi R, Lezon TR. Predicting the Effects of Drug Combinations Using Probabilistic Matrix Factorization. FRONTIERS IN BIOINFORMATICS 2021; 1:708815. [PMID: 36303743 PMCID: PMC9581062 DOI: 10.3389/fbinf.2021.708815] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 07/30/2021] [Indexed: 12/12/2022] Open
Abstract
Drug development is costly and time-consuming, and developing novel practical strategies for creating more effective treatments is imperative. One possible solution is to prescribe drugs in combination. Synergistic drug combinations could allow lower doses of each constituent drug, reducing adverse reactions and drug resistance. However, it is not feasible to sufficiently test every combination of drugs for a given illness to determine promising synergistic combinations. Since there is a finite amount of time and resources available for finding synergistic combinations, a model that can identify synergistic combinations from a limited subset of all available combinations could accelerate development of therapeutics. By applying recommender algorithms, such as the low-rank matrix completion algorithm Probabilistic Matrix Factorization (PMF), it may be possible to identify synergistic combinations from partial information of the drug interactions. Here, we use PMF to predict the efficacy of two-drug combinations using the NCI ALMANAC, a robust collection of pairwise drug combinations of 104 FDA-approved anticancer drugs against 60 common cancer cell lines. We find that PMF is able predict drug combination efficacy with high accuracy from a limited set of combinations and is robust to changes in the individual training data. Moreover, we propose a new PMF-guided experimental design to detect all synergistic combinations without testing every combination.
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Piyawajanusorn C, Nguyen LC, Ghislat G, Ballester PJ. A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling. Brief Bioinform 2021; 22:6343527. [PMID: 34368843 DOI: 10.1093/bib/bbab312] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/25/2021] [Accepted: 07/20/2021] [Indexed: 12/16/2022] Open
Abstract
A central goal of precision oncology is to administer an optimal drug treatment to each cancer patient. A common preclinical approach to tackle this problem has been to characterize the tumors of patients at the molecular and drug response levels, and employ the resulting datasets for predictive in silico modeling (mostly using machine learning). Understanding how and why the different variants of these datasets are generated is an important component of this process. This review focuses on providing such introduction aimed at scientists with little previous exposure to this research area.
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Affiliation(s)
- Chayanit Piyawajanusorn
- Cancer Research Center of Marseille, INSERM U1068, F-13009 Marseille, France.,Institut Paoli-Calmettes, F-13009 Marseille, France.,Aix-Marseille Université, F-13284 Marseille, France.,CNRS UMR7258, F-13009 Marseille, France.,Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Linh C Nguyen
- Cancer Research Center of Marseille, INSERM U1068, F-13009 Marseille, France.,Institut Paoli-Calmettes, F-13009 Marseille, France.,Aix-Marseille Université, F-13284 Marseille, France.,CNRS UMR7258, F-13009 Marseille, France.,Department of Life Sciences, University of Science and Technology of Hanoi, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Ghita Ghislat
- U1104, CNRS UMR7280, Centre d'Immunologie de Marseille-Luminy, Inserm, Marseille, France
| | - Pedro J Ballester
- Cancer Research Center of Marseille, INSERM U1068, F-13009 Marseille, France.,Institut Paoli-Calmettes, F-13009 Marseille, France.,Aix-Marseille Université, F-13284 Marseille, France.,CNRS UMR7258, F-13009 Marseille, France
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Papachristou F, Anninou N, Koukoulis G, Paraskakis S, Sertaridou E, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha A. Differential effects of cisplatin combined with the flavonoid apigenin on HepG2, Hep3B, and Huh7 liver cancer cell lines. Mutat Res 2021; 866:503352. [PMID: 33985696 DOI: 10.1016/j.mrgentox.2021.503352] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 03/16/2021] [Accepted: 03/22/2021] [Indexed: 02/07/2023]
Abstract
The potential of apigenin (APG) to enhance cisplatin's (CDDP) chemotherapeutic efficacy was investigated in HepG2, Hep3B, and Huh7 liver cancer cell lines. The presence of 20 μM APG sensitized all cell lines to CDDP treatment (degree of sensitization based on the MTT assay: HepG2>Huh7>Hep3B). As reflected by sister chromatid exchange levels, the degree of genetic instability as well as DNA repair by homologous recombination differed among cell lines. CDDP and 20 μM APG cotreatment exhibited a synergistic genotoxic effect on Hep3B cells and a less than additive effect on HepG2 and Huh7 cells. Cell cycle delays were noticed during the first mitotic division in Hep3B and Huh7 cells and the second mitotic division in HepG2 cells. CDDP and CDDP + APG treatments reduced the clonogenic capacity of all cell lines; however, there was a discordance in drug sensitivity compared with the MMT assay. Furthermore, a senescence-like phenotype was induced, especially in Hep3B and Huh7 cells. Unlike CDDP monotherapy, the combined treatment exhibited a significant anti-invasive and anti-migratory action in all cancer cell lines. The fact that the three liver cancer cell lines responded differently, yet positively, to CDDP + APG cotreatment could be attributed to variations they present in gene expression. Complex mechanisms seem to influence cellular responses and cell fate.
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Affiliation(s)
- Fotini Papachristou
- Laboratory of Experimental Surgery and Surgical Research, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, 68 100, Greece; Postgraduate Program in Hepatobiliary and Pancreatic Surgery, 2nd Department of Surgery, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, 68 100, Greece.
| | - Nikolia Anninou
- Laboratory of Experimental Surgery and Surgical Research, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, 68 100, Greece
| | - Georgios Koukoulis
- Postgraduate Program in Hepatobiliary and Pancreatic Surgery, 2nd Department of Surgery, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, 68 100, Greece
| | - Stefanos Paraskakis
- Postgraduate Program in Hepatobiliary and Pancreatic Surgery, 2nd Department of Surgery, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, 68 100, Greece
| | - Eleni Sertaridou
- Postgraduate Program in Hepatobiliary and Pancreatic Surgery, 2nd Department of Surgery, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, 68 100, Greece
| | - Christos Tsalikidis
- Postgraduate Program in Hepatobiliary and Pancreatic Surgery, 2nd Department of Surgery, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, 68 100, Greece
| | - Michael Pitiakoudis
- Postgraduate Program in Hepatobiliary and Pancreatic Surgery, 2nd Department of Surgery, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, 68 100, Greece
| | - Constantinos Simopoulos
- Laboratory of Experimental Surgery and Surgical Research, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, 68 100, Greece; Postgraduate Program in Hepatobiliary and Pancreatic Surgery, 2nd Department of Surgery, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, 68 100, Greece
| | - Alexandra Tsaroucha
- Laboratory of Experimental Surgery and Surgical Research, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, 68 100, Greece; Postgraduate Program in Hepatobiliary and Pancreatic Surgery, 2nd Department of Surgery, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, 68 100, Greece
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7
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Fomenko A, Kazantsev S, Lozhkomoev AS, Rodkevich NG, Miller AA. Influence of Morphology and Textural Characteristics of γ-Al2O3 Nanostructures on the Potentiation of Doxorubicin. J CLUST SCI 2021. [DOI: 10.1007/s10876-021-02009-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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8
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Miladiyah I, Yuanita E, Nuryadi S, Jumina J, Haryana SM, Mustofa M. Synergistic Effect of 1,3,6-Trihydroxy-4,5,7-Trichloroxanthone in Combination with Doxorubicin on B-Cell Lymphoma Cells and Its Mechanism of Action Through Molecular Docking. Curr Ther Res Clin Exp 2020; 92:100576. [PMID: 32123546 PMCID: PMC7037593 DOI: 10.1016/j.curtheres.2020.100576] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 01/22/2020] [Indexed: 12/11/2022] Open
Abstract
Background The increasing rate of cancer chemoresistance and adverse side effects of therapy have led to the wide use of various chemotherapeutic combinations in cancer management, including lymphoid malignancy. Objective We investigated the effects of a combination of 1,3,6-trihydroxy-4,5,7-trichloroxanthone (TTX) and doxorubicin on the Raji lymphoma cell line. Methods Raji cells were treated with different concentrations of TTX, doxorubicin, or combinations thereof. Cancer cell growth inhibition was evaluated using 3-(4,5-dimethyltiazol-2-yl)-2,5- diphenyltetrazolium bromide/MTT assay to determine the half-maximal inhibitory concentration. Combination index values were calculated using CompuSyn (ComboSyn, Inc, Paramus, NJ). Molecular docking was conducted using a Protein-Ligand ANT System. Results The mean (SD) half-maximal inhibitory concentration values of TTX and doxorubicin were 15.948 (3.101) µM and 25.432 (1.417) µM, respectively. The combination index values of the different combinations ranged from 0.057 to 0.285, indicating strong to very strong synergistic effects. The docking study results reveal that TTX docks at the active site of Raf-1 and c-Jun N-kinase receptors with predicted free energies of binding of -79.37 and -75.42 kcal/mol, respectively. Conclusions The xanthone-doxorubicin combination showed promising in vitro activity against lymphoma cells. The results also indicate that the TTX and doxorubicin combination's effect was due to the interaction between TTX with Raf-1 and c-Jun N-kinase receptors, 2 determinants of doxorubicin resistance progression.
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Affiliation(s)
- Isnatin Miladiyah
- Pharmacology Department, Faculty of Medicine, Universitas Islam Indonesia, Yogyakarta, Indonesia
| | - Emmy Yuanita
- Chemistry Department, Faculty of Mathematics and Natural Sciences, Mataram University, Mataram, Indonesia
| | - Satyo Nuryadi
- Electrical Engineering Department, Faculty of Information Technology and Electrical, Technology University of Yogyakarta, Yogyakarta, Indonesia
| | - Jumina Jumina
- Chemistry Department, Faculty of Mathematics and Natural Sciences, Gadjah Mada University, Yogyakarta, Indonesia
| | - Sofia Mubarika Haryana
- Histology and Cell Biology Department, Faculty of Medicine, Public Health, and Nursing, Gadjah Mada University, Yogyakarta, Indonesia
| | - Mustofa Mustofa
- Pharmacology and Therapeutic Department, Faculty of Medicine, Public Health, and Nursing, Gadjah Mada University, Yogyakarta, Indonesia
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Weiss A, Le Roux-Bourdieu M, Zoetemelk M, Ramzy GM, Rausch M, Harry D, Miljkovic-Licina M, Falamaki K, Wehrle-Haller B, Meraldi P, Nowak-Sliwinska P. Identification of a Synergistic Multi-Drug Combination Active in Cancer Cells via the Prevention of Spindle Pole Clustering. Cancers (Basel) 2019; 11:E1612. [PMID: 31652588 PMCID: PMC6826636 DOI: 10.3390/cancers11101612] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 10/14/2019] [Accepted: 10/16/2019] [Indexed: 02/06/2023] Open
Abstract
A major limitation of clinically used cancer drugs is the lack of specificity resulting in toxicity. To address this, we performed a phenotypically-driven screen to identify optimal multidrug combinations acting with high efficacy and selectivity in clear cell renal cell carcinoma (ccRCC). The search was performed using the Therapeutically Guided Multidrug Optimization (TGMO) method in ccRCC cells (786-O) and nonmalignant renal cells and identified a synergistic low-dose four-drug combination (C2) with high efficacy and negligible toxicity. We discovered that C2 inhibits multipolar spindle pole clustering, a survival mechanism employed by cancer cells with spindle abnormalities. This phenotype was also observed in 786-O cells resistant to sunitinib, the first line ccRCC treatment, as well as in melanoma cells with distinct percentages of supernumerary centrosomes. We conclude that C2-treatment shows a high efficacy in cells prone to form multipolar spindles. Our data suggest a highly effective and selective C2 treatment strategy for malignant and drug-resistant cancers.
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Affiliation(s)
- Andrea Weiss
- Institute of Pharmaceutical Sciences of Western Switzerland, Faculty of Sciences, University of Geneva, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
- Translational Research Centre in Oncohaematology, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
| | - Morgan Le Roux-Bourdieu
- Translational Research Centre in Oncohaematology, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
- Department of Cell Physiology and Metabolism, University of Geneva Medical School, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
| | - Marloes Zoetemelk
- Institute of Pharmaceutical Sciences of Western Switzerland, Faculty of Sciences, University of Geneva, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
- Translational Research Centre in Oncohaematology, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
| | - George M Ramzy
- Institute of Pharmaceutical Sciences of Western Switzerland, Faculty of Sciences, University of Geneva, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
| | - Magdalena Rausch
- Institute of Pharmaceutical Sciences of Western Switzerland, Faculty of Sciences, University of Geneva, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
- Translational Research Centre in Oncohaematology, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
| | - Daniela Harry
- Department of Cell Physiology and Metabolism, University of Geneva Medical School, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
| | - Marijana Miljkovic-Licina
- Translational Research Centre in Oncohaematology, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
- Department of Pathology and Immunology, University of Geneva Medical School, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
| | - Katayoun Falamaki
- Department of Cell Physiology and Metabolism, University of Geneva Medical School, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
| | - Bernard Wehrle-Haller
- Translational Research Centre in Oncohaematology, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
- Department of Cell Physiology and Metabolism, University of Geneva Medical School, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
| | - Patrick Meraldi
- Translational Research Centre in Oncohaematology, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
- Department of Cell Physiology and Metabolism, University of Geneva Medical School, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
| | - Patrycja Nowak-Sliwinska
- Institute of Pharmaceutical Sciences of Western Switzerland, Faculty of Sciences, University of Geneva, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
- Translational Research Centre in Oncohaematology, 1 Rue Michel-Servet, CMU, 1211 Geneva 4, Switzerland.
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Richa K, Karmaker R, Longkumer N, Das V, Bhuyan PJ, Pal M, Sinha UB. Synthesis, In Vitro Evaluation, Molecular Docking and DFT Studies of Some Phenyl Isothiocyanates as Anticancer Agents. Anticancer Agents Med Chem 2019; 19:2211-2222. [PMID: 31566135 DOI: 10.2174/1871520619666190930122137] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 06/11/2019] [Accepted: 06/30/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Isothiocyanates (ITCs) are small molecules that are important in synthetic organic chemistry, but their actual importance lies in their potential as anti-carcinogens. Through this piece of work, an effort was made to assess the anti-cancer activity of some simple ITCs which can be synthesized through easy greener pathways. METHODS Cell proliferation assay was performed on ovarian cancer cells (PA-1) and non-tumorigenic ovarian epithelial cells (IOSE-364). Furthermore, qRT-PCR for transcript expression levels of Spindlin1 and caspases in ovarian cancer cells and cell cycle analysis was performed. In silico studies were incorporated to understand the mode of ligand-protein interaction, ADME/Toxicity and drug-likeliness parameters. Density functional theory studies have been also been employed on the ITCs to assess their efficiency in anticancer activity. RESULTS An inexpensive, environmentally benign pathway has been developed for synthesizing a series of ITCs. Among the synthesized ITCs, NC6 showed better cytotoxic effects as compared to its counterparts. Novel findings revealed that NC6 had 5-folds lower transcript expression levels of Spindlin1 and induced caspases 3 and 7 expressions assessed by qRT-PCR in ovarian cancer cells. Furthermore, flow cytometry assay showed the cell cycle arrest at G1/S phase of cell cycle. The molecular docking studies revealed favorable binding affinities and the physiochemical parameters were predicted to be compatible with drug-likeliness. CONCLUSION The results demonstrated the possibility that small isothiocyanate molecules which can be synthesized by a simple green methodology, can pose as promising candidates for their application as anticancer agents.
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Affiliation(s)
- Kikoleho Richa
- Department of Chemistry, Nagaland University, Lumami-798627, Nagaland, India.,Bioinformatics Facility Centre, Nagaland University, Lumami-798627, Nagaland, India
| | - Rituparna Karmaker
- Department of Chemistry, Nagaland University, Lumami-798627, Nagaland, India
| | - Naruti Longkumer
- Department of Chemistry, Nagaland University, Lumami-798627, Nagaland, India
| | - Vishal Das
- Biological Sciences and Technology Division, Biotechnology Group, CSIR-North East Institute of Science and Technology (NEIST), Academy of Scientific and Innovative Research, Jorhat, Assam-785006, India
| | - Pulak J Bhuyan
- Chemical Sciences and Technology Division, CSIR- North East Institute of Science and Technology (NEIST), Jorhat, Assam-785006, India
| | - Mintu Pal
- Biological Sciences and Technology Division, Biotechnology Group, CSIR-North East Institute of Science and Technology (NEIST), Academy of Scientific and Innovative Research, Jorhat, Assam-785006, India
| | - Upasana B Sinha
- Department of Chemistry, Nagaland University, Lumami-798627, Nagaland, India
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Sidorov P, Naulaerts S, Ariey-Bonnet J, Pasquier E, Ballester PJ. Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data. Front Chem 2019; 7:509. [PMID: 31380352 PMCID: PMC6646421 DOI: 10.3389/fchem.2019.00509] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 07/02/2019] [Indexed: 12/15/2022] Open
Abstract
Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations). Each cell line is modeled using primarily two machine learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5,000 pair-wise drug combinations, 60 cell lines, 4 types of models, and 5 types of chemical features. The application of a powerful, yet uncommonly used, RF-specific technique for reliability prediction is also investigated. The evaluation of these models shows that it is possible to predict the synergy of unseen drug combinations with high accuracy (Pearson correlations between 0.43 and 0.86 depending on the considered cell line, with XGBoost providing slightly better predictions than RF). We have also found that restricting to the most reliable synergy predictions results in at least 2-fold error decrease with respect to employing the best learning algorithm without any reliability estimation. Alkylating agents, tyrosine kinase inhibitors and topoisomerase inhibitors are the drugs whose synergy with other partner drugs are better predicted by the models. Despite its leading size, NCI-ALMANAC comprises an extremely small part of all conceivable combinations. Given their accuracy and reliability estimation, the developed models should drastically reduce the number of required in vitro tests by predicting in silico which of the considered combinations are likely to be synergistic.
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Affiliation(s)
- Pavel Sidorov
- CRCM, INSERM, Cancer Research Center of Marseille, Institut Paoli-Calmettes, Aix-Marseille Univ, CNRS, Marseille, France
| | - Stefan Naulaerts
- CRCM, INSERM, Cancer Research Center of Marseille, Institut Paoli-Calmettes, Aix-Marseille Univ, CNRS, Marseille, France
- Department of Tumor Immunology, Institut de Duve, Bruxelles, Belgium
| | - Jérémy Ariey-Bonnet
- CRCM, INSERM, Cancer Research Center of Marseille, Institut Paoli-Calmettes, Aix-Marseille Univ, CNRS, Marseille, France
| | - Eddy Pasquier
- CRCM, INSERM, Cancer Research Center of Marseille, Institut Paoli-Calmettes, Aix-Marseille Univ, CNRS, Marseille, France
| | - Pedro J. Ballester
- CRCM, INSERM, Cancer Research Center of Marseille, Institut Paoli-Calmettes, Aix-Marseille Univ, CNRS, Marseille, France
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Chantzi E, Jarvius M, Niklasson M, Segerman A, Gustafsson MG. COMBImage2: a parallel computational framework for higher-order drug combination analysis that includes automated plate design, matched filter based object counting and temporal data mining. BMC Bioinformatics 2019; 20:304. [PMID: 31164078 PMCID: PMC6549340 DOI: 10.1186/s12859-019-2908-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Accepted: 05/21/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Pharmacological treatment of complex diseases using more than two drugs is commonplace in the clinic due to better efficacy, decreased toxicity and reduced risk for developing resistance. However, many of these higher-order treatments have not undergone any detailed preceding in vitro evaluation that could support their therapeutic potential and reveal disease related insights. Despite the increased medical need for discovery and development of higher-order drug combinations, very few reports from systematic large-scale studies along this direction exist. A major reason is lack of computational tools that enable automated design and analysis of exhaustive drug combination experiments, where all possible subsets among a panel of pre-selected drugs have to be evaluated. RESULTS Motivated by this, we developed COMBImage2, a parallel computational framework for higher-order drug combination analysis. COMBImage2 goes far beyond its predecessor COMBImage in many different ways. In particular, it offers automated 384-well plate design, as well as quality control that involves resampling statistics and inter-plate analyses. Moreover, it is equipped with a generic matched filter based object counting method that is currently designed for apoptotic-like cells. Furthermore, apart from higher-order synergy analyses, COMBImage2 introduces a novel data mining approach for identifying interesting temporal response patterns and disentangling higher- from lower- and single-drug effects. COMBImage2 was employed in the context of a small pilot study focused on the CUSP9v4 protocol, which is currently used in the clinic for treatment of recurrent glioblastoma. For the first time, all 246 possible combinations of order 4 or lower of the 9 single drugs consisting the CUSP9v4 cocktail, were evaluated on an in vitro clonal culture of glioma initiating cells. CONCLUSIONS COMBImage2 is able to automatically design and robustly analyze exhaustive and in general higher-order drug combination experiments. Such a versatile video microscopy oriented framework is likely to enable, guide and accelerate systematic large-scale drug combination studies not only for cancer but also other diseases.
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Affiliation(s)
- Efthymia Chantzi
- Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala, Sweden.
| | - Malin Jarvius
- Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala, Sweden.,SciLifeLab Drug Discovery and Development, In Vitro Systems Pharmacology Facility, Uppsala University, Uppsala, Sweden
| | - Mia Niklasson
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Anna Segerman
- Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala, Sweden.,Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Mats G Gustafsson
- Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala, Sweden
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Effect of Spheroidal Age on Sorafenib Diffusivity and Toxicity in a 3D HepG2 Spheroid Model. Sci Rep 2019; 9:4863. [PMID: 30890741 PMCID: PMC6425026 DOI: 10.1038/s41598-019-41273-3] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 03/04/2019] [Indexed: 01/08/2023] Open
Abstract
The enhanced predictive power of 3D multi-cellular spheroids in comparison to conventional monolayer cultures makes them a promising drug screening tool. However, clinical translation for pharmacology and toxicology is lagging its technological progression. Even though spheroids show a biological complexity resembling native tissue, standardization and validation of drug screening protocols are influenced by continuously changing physiological parameters during spheroid formation. Such cellular heterogeneities impede the comparability of drug efficacy studies and toxicological screenings. In this paper, we demonstrated that aside from already well-established physiological parameters, spheroidal age is an additional critical parameter that impacts drug diffusivity and toxicity in 3D cell culture models. HepG2 spheroids were generated and maintained on a self-assembled ultra-low attachment nanobiointerface and characterized regarding time-dependent changes in morphology, functionality as well as anti-cancer drug resistance. We demonstrated that spheroidal aging directly influences drug response due to the evolution of spheroid micro-structure and organo-typic functions, that alter inward diffusion, thus drug uptake.
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14
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Ghosh S, Lalani R, Patel V, Bardoliwala D, Maiti K, Banerjee S, Bhowmick S, Misra A. Combinatorial nanocarriers against drug resistance in hematological cancers: Opportunities and emerging strategies. J Control Release 2019; 296:114-139. [DOI: 10.1016/j.jconrel.2019.01.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 01/10/2019] [Accepted: 01/11/2019] [Indexed: 12/16/2022]
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15
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Chantzi E, Jarvius M, Niklasson M, Segerman A, Gustafsson MG. COMBImage: a modular parallel processing framework for pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies. BMC Bioinformatics 2018; 19:453. [PMID: 30477419 PMCID: PMC6257977 DOI: 10.1186/s12859-018-2458-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Accepted: 10/03/2018] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Large-scale pairwise drug combination analysis has lately gained momentum in drug discovery and development projects, mainly due to the employment of advanced experimental-computational pipelines. This is fortunate as drug combinations are often required for successful treatment of complex diseases. Furthermore, most new drugs cannot totally replace the current standard-of-care medication, but rather have to enter clinical use as add-on treatment. However, there is a clear deficiency of computational tools for label-free and temporal image-based drug combination analysis that go beyond the conventional but relatively uninformative end point measurements. RESULTS COMBImage is a fast, modular and instrument independent computational framework for in vitro pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies. Jointly with automated analyses of temporal changes in cell morphology and confluence, it performs and displays conventional cell viability and synergy end point analyses. The image processing algorithms are parallelized using Google's MapReduce programming model and optimized with respect to method-specific tuning parameters. COMBImage is shown to process time-lapse microscopy movies from 384-well plates within minutes on a single quad core personal computer. This framework was employed in the context of an ongoing drug discovery and development project focused on glioblastoma multiforme; the most deadly form of brain cancer. Interesting add-on effects of two investigational cytotoxic compounds when combined with vorinostat were revealed on recently established clonal cultures of glioma-initiating cells from patient tumor samples. Therapeutic synergies, when normal astrocytes were used as a toxicity cell model, reinforced the pharmacological interest regarding their potential clinical use. CONCLUSIONS COMBImage enables, for the first time, fast and optimized pairwise drug combination analyses of temporal changes in label-free video microscopy movies. Providing this jointly with conventional cell viability based end point analyses, it could help accelerating and guiding any drug discovery and development project, without use of cell labeling and the need to employ a particular live cell imaging instrument.
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Affiliation(s)
- Efthymia Chantzi
- Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala, Sweden
| | - Malin Jarvius
- Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala, Sweden
- SciLifeLab Drug Discovery and Development, In Vitro Systems Pharmacology Facility, Uppsala University, Uppsala, Sweden
| | - Mia Niklasson
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Anna Segerman
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Mats G. Gustafsson
- Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala, Sweden
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Chu PC, Lin PC, Wu HY, Lin KT, Wu C, Bekaii-Saab T, Lin YJ, Lee CT, Lee JC, Chen CS. Mutant KRAS promotes liver metastasis of colorectal cancer, in part, by upregulating the MEK-Sp1-DNMT1-miR-137-YB-1-IGF-IR signaling pathway. Oncogene 2018; 37:3440-3455. [PMID: 29559746 DOI: 10.1038/s41388-018-0222-3] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Revised: 11/20/2017] [Accepted: 02/25/2018] [Indexed: 02/07/2023]
Abstract
Although the role of insulin-like growth factor-I receptor (IGF-IR) in promoting colorectal liver metastasis is known, the mechanism by which IGF-IR is upregulated in colorectal cancer (CRC) is not defined. In this study, we obtained evidence that mutant KRAS transcriptionally activates IGF-IR gene expression through Y-box-binding protein (YB)-1 upregulation via a novel MEK-Sp1-DNMT1-miR-137 pathway in CRC cells. The mechanistic link between the tumor suppressive miR-137 and the translational regulation of YB-1 is intriguing because epigenetic silencing of miR-137 represents an early event in colorectal carcinogenesis due to promoter hypermethylation. This proposed signaling axis was further verified by the immunohistochemical evaluations of liver metastases from a cohort of 46 KRAS mutant CRC patients, which showed a significant correlation in the expression levels among Sp1, miR-137, YB-1, and IGF-1R. Moreover, suppression of the expression of YB-1 and IGF-IR via genetic knockdown or the pharmacological inhibition of MEK hampers KRAS-driven colorectal liver metastasis in our animal model studies. From a translational perspective, the identification of this KRAS-driven pathway might provide a mechanistic rationale for the use of a MEK inhibitor as an adjuvant, in combination with standard of care, to prevent the recurrence of colorectal liver metastasis in KRAS mutant CRC patients after receiving liver resection, which warrants further investigation.
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Affiliation(s)
- Po-Chen Chu
- Institute of Biological Chemistry, Academia Sinica, 11529, Taipei, Taiwan
- Institute of New Drug Development, College of Biopharmaceutical and Food Sciences, China Medical University, 40402, Taichung, Taiwan
| | - Peng-Chan Lin
- Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 70403, Tainan, Taiwan
| | - Hsing-Yu Wu
- Institute of Biological Chemistry, Academia Sinica, 11529, Taipei, Taiwan
- Institute of Biochemical Sciences, College of Life Science, National Taiwan University, 10617, Taipei, Taiwan
| | - Kuen-Tyng Lin
- Institute of Biological Chemistry, Academia Sinica, 11529, Taipei, Taiwan
| | - Christina Wu
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Tanios Bekaii-Saab
- Mayo Clinic College of Medicine and Science, Mayo Clinic Cancer Center, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Yih-Jyh Lin
- Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 70403, Tainan, Taiwan
| | - Chung-Ta Lee
- Department of Pathology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 70403, Tainan, Taiwan
| | - Jeng-Chang Lee
- Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 70403, Tainan, Taiwan
| | - Ching-Shih Chen
- Institute of Biological Chemistry, Academia Sinica, 11529, Taipei, Taiwan.
- Institute of New Drug Development, College of Biopharmaceutical and Food Sciences, China Medical University, 40402, Taichung, Taiwan.
- Institute of Biochemical Sciences, College of Life Science, National Taiwan University, 10617, Taipei, Taiwan.
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He L, Tang J, Andersson EI, Timonen S, Koschmieder S, Wennerberg K, Mustjoki S, Aittokallio T. Patient-Customized Drug Combination Prediction and Testing for T-cell Prolymphocytic Leukemia Patients. Cancer Res 2018; 78:2407-2418. [DOI: 10.1158/0008-5472.can-17-3644] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 01/17/2018] [Accepted: 02/20/2018] [Indexed: 11/16/2022]
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Lau MF, Vellasamy S, Chua KH, Sabaratnam V, Kuppusamy UR. Rosiglitazone diminishes the high-glucose-induced modulation of 5-fluorouracil cytotoxicity in colorectal cancer cells. EXCLI JOURNAL 2018; 17:186-199. [PMID: 29743857 PMCID: PMC5938530 DOI: 10.17179/excli2018-1011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Accepted: 01/22/2018] [Indexed: 02/06/2023]
Abstract
Colorectal cancer (CRC) is the third most leading cause of morbidity and mortality throughout the world. 5-fluorouracil (5-FU), which is often administrated to disrupt carcinogenesis, was found to elevate blood glucose level among CRC patients. Thus, this study was conducted to evaluate the influence of rosiglitazone on antiproliferative effect of 5-FU using cellular model. Two human colonic carcinoma cell lines (HCT 116 and HT 29) were cultured in the presence of 5-FU, rosiglitazone or in combination under normal and high glucose concentration. The drug cytotoxicity was evaluated using the MTT assay whereas the assessment of cell cycle was carried out using the flow cytometry technique. Combination index (CI) method was used to determine the drug interaction between rosiglitazone and 5-FU. High glucose diminished the cytotoxic effect of 5-FU but at a high drug dosage, this effect could be overcome. Cell cycle analysis demonstrated that 5-FU and rosiglitazone caused G1-phase arrest and S-phase arrest, respectively. CI values indicated that rosiglitazone exerted synergistic effect on 5-FU regardless of glucose levels. This study is the first to demonstrate the influence of rosiglitazone on cytotoxicity of 5-FU under normal or high glucose level. Rosiglitazone may be a promising drug for enhancing the efficacy of 5-FU in the treatment of CRC associated with hyperglycemia.
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Affiliation(s)
- Meng-Fei Lau
- Department of Biomedical Science, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia.,Mushroom Research Centre, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Shalini Vellasamy
- Department of Biomedical Science, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Kek-Heng Chua
- Department of Biomedical Science, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia.,Mushroom Research Centre, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Vikineswary Sabaratnam
- Institute of Biological Science, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia.,Mushroom Research Centre, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Umah Rani Kuppusamy
- Department of Biomedical Science, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia.,Mushroom Research Centre, University of Malaya, 50603 Kuala Lumpur, Malaysia
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Kischkel FC, Meyer C, Eich J, Nassir M, Mentze M, Braicu I, Kopp-Schneider A, Sehouli J. Prediction of clinical response to drugs in ovarian cancer using the chemotherapy resistance test (CTR-test). J Ovarian Res 2017; 10:72. [PMID: 29078793 PMCID: PMC5658930 DOI: 10.1186/s13048-017-0365-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 10/04/2017] [Indexed: 01/08/2023] Open
Abstract
Background In order to validate if the test result of the Chemotherapy Resistance Test (CTR-Test) is able to predict the resistances or sensitivities of tumors in ovarian cancer patients to drugs, the CTR-Test result and the corresponding clinical response of individual patients were correlated retrospectively. Results were compared to previous recorded correlations. Methods The CTR-Test was performed on tumor samples from 52 ovarian cancer patients for specific chemotherapeutic drugs. Patients were treated with monotherapies or drug combinations. Resistances were classified as extreme (ER), medium (MR) or slight (SR) resistance in the CTR-Test. Combination treatment resistances were transformed by a scoring system into these classifications. Results Accurate sensitivity prediction was accomplished in 79% of the cases and accurate prediction of resistance in 100% of the cases in the total data set. The data set of single agent treatment and drug combination treatment were analyzed individually. Single agent treatment lead to an accurate sensitivity in 44% of the cases and the drug combination to 95% accuracy. The detection of resistances was in both cases to 100% correct. ROC curve analysis indicates that the CTR-Test result correlates with the clinical response, at least for the combination chemotherapy. Those values are similar or better than the values from a publication from 1990. Conclusions Chemotherapy resistance testing in vitro via the CTR-Test is able to accurately detect resistances in ovarian cancer patients. These numbers confirm and even exceed results published in 1990. Better sensitivity detection might be caused by a higher percentage of drug combinations tested in 2012 compared to 1990. Our study confirms the functionality of the CTR-Test to plan an efficient chemotherapeutic treatment for ovarian cancer patients. Electronic supplementary material The online version of this article (10.1186/s13048-017-0365-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | | | | | - Mani Nassir
- Charité Berlin, Gynecology Department, Virchow Campus Berlin, Berlin, Germany
| | - Monika Mentze
- Charité Berlin, Gynecology Department, Virchow Campus Berlin, Berlin, Germany
| | - Ioana Braicu
- Charité Berlin, Gynecology Department, Virchow Campus Berlin, Berlin, Germany
| | | | - Jalid Sehouli
- Charité Berlin, Gynecology Department, Virchow Campus Berlin, Berlin, Germany
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20
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Won SJ, Yen CH, Hsieh HW, Chang SW, Lin CN, Huang CYF, Su CL. Using connectivity map to identify natural lignan justicidin A as a NF-κB suppressor. J Funct Foods 2017. [DOI: 10.1016/j.jff.2017.04.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
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Institutional profile: the national Swedish academic drug discovery & development platform at SciLifeLab. Future Sci OA 2017; 3:FSO176. [PMID: 28670468 PMCID: PMC5481862 DOI: 10.4155/fsoa-2017-0013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Accepted: 01/23/2017] [Indexed: 11/29/2022] Open
Abstract
The Science for Life Laboratory Drug Discovery and Development Platform (SciLifeLab DDD) was established in Stockholm and Uppsala, Sweden, in 2014. It is one of ten platforms of the Swedish national SciLifeLab which support projects run by Swedish academic researchers with large-scale technologies for molecular biosciences with a focus on health and environment. SciLifeLab was created by the coordinated effort of four universities in Stockholm and Uppsala: Stockholm University, Karolinska Institutet, KTH Royal Institute of Technology and Uppsala University, and has recently expanded to other Swedish university locations. The primary goal of the SciLifeLab DDD is to support selected academic discovery and development research projects with tools and resources to discover novel lead therapeutics, either molecules or human antibodies. Intellectual property developed with the help of SciLifeLab DDD is wholly owned by the academic research group. The bulk of SciLifeLab DDD's research and service activities are funded from the Swedish state, with only consumables paid by the academic research group through individual grants.
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22
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Kischkel FC, Eich J, Meyer CI, Weidemüller P, Krapfl J, Yassin-Kelepir R, Job L, Fraefel M, Braicu I, Kopp-Schneider A, Sehouli J, De Wilde RL. New in vitro system to predict chemotherapeutic efficacy of drug combinations in fresh tumor samples. PeerJ 2017; 5:e3030. [PMID: 28265509 PMCID: PMC5337084 DOI: 10.7717/peerj.3030] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Accepted: 01/25/2017] [Indexed: 12/13/2022] Open
Abstract
Background To find the best individual chemotherapy for cancer patients, the efficacy of different chemotherapeutic drugs can be predicted by pretesting tumor samples in vitro via the chemotherapy-resistance (CTR)-Test®. Although drug combinations are widely used among cancer therapy, so far only single drugs are tested by this and other tests. However, several first line chemotherapies are combining two or more chemotherapeutics, leading to the necessity of drug combination testing methods. Methods We established a system to measure and predict the efficacy of chemotherapeutic drug combinations with the help of the Loewe additivity concept in combination with the CTR-test. A combination is measured by using half of the monotherapy’s concentration of both drugs simultaneously. With this method, the efficacy of a combination can also be calculated based on single drug measurements. Results The established system was tested on a data set of ovarian carcinoma samples using the combination carboplatin and paclitaxel and confirmed by using other tumor species and chemotherapeutics. Comparing the measured and the calculated values of the combination testings revealed a high correlation. Additionally, in 70% of the cases the measured and the calculated values lead to the same chemotherapeutic resistance category of the tumor. Conclusion Our data suggest that the best drug combination consists of the most efficient single drugs and the worst drug combination of the least efficient single drugs. Our results showed that single measurements are sufficient to predict combinations in specific cases but there are exceptions in which it is necessary to measure combinations, which is possible with the presented system.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Ioana Braicu
- Gynecology Department, Charité Berlin, Virchow Campus Berlin, Germany
| | | | - Jalid Sehouli
- Gynecology Department, Charité Berlin, Virchow Campus Berlin, Germany
| | - Rudy Leon De Wilde
- University Hospital for Gynecology, Carl von Ossietzky University Oldenburg, Germany
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Combination screening in vitro identifies synergistically acting KP372-1 and cytarabine against acute myeloid leukemia. Biochem Pharmacol 2016; 118:40-49. [PMID: 27565890 DOI: 10.1016/j.bcp.2016.08.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2016] [Accepted: 08/22/2016] [Indexed: 11/20/2022]
Abstract
Cytogenetic lesions often alter kinase signaling in acute myeloid leukemia (AML) and the addition of kinase inhibitors to the treatment arsenal is of interest. We have screened a kinase inhibitor library and performed combination testing to find promising drug-combinations for synergistic killing of AML cells. Cytotoxicity of 160 compounds in the library InhibitorSelect™ 384-Well Protein Kinase Inhibitor I was measured using the fluorometric microculture cytotoxicity assay (FMCA) in three AML cell lines. The 15 most potent substances were evaluated for dose-response. The 6 most cytotoxic compounds underwent combination synergy analysis based on the FMCA readouts after either simultaneous or sequential drug addition in AML cell lines. The 4 combinations showing the highest level of synergy were evaluated in 5 primary AML samples. Synergistic calculations were performed using the combination interaction analysis package COMBIA, written in R, using the Bliss independence model. Based on obtained results, an iterative combination search was performed using the therapeutic algorithmic combinatorial screen (TACS) algorithm. Of 160 substances, cell survival was ⩽50% at <0.5μM for Cdk/Crk inhibitor, KP372-1, synthetic fascaplysin, herbimycin A, PDGF receptor tyrosine kinase inhibitor IV and reference-drug cytarabine. KP372-1, synthetic fascaplysin or herbimycin A obtained synergy when combined with cytarabine in AML cell lines MV4-11 and HL-60. KP372-1 added 24h before cytarabine gave similar results in patient cells. The iterative search gave further improved synergy between cytarabine and KP372-1. In conclusion, our in vitro studies suggest that combining KP372-1 and cytarabine is a potent and synergistic drug combination in AML.
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