Synergistic Effects Of Drug Combinations Research Articles

MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction

Drug combination therapy is generally more effective than monotherapy in the field of cancer treatment. However, screening for effective synergistic combinations from a wide range of drug combinations is particularly important given the increase in the number of available drug classes and potential drug-drug interactions. Existing methods for predicting the synergistic effects of drug combinations primarily focus on extracting structural features of drug molecules and cell lines, but neglect the interaction mechanisms between cell lines and drug combinations. Consequently, there is a deficiency in comprehensive understanding of the synergistic effects of drug combinations. To address this issue, we propose a drug combination synergy prediction model based on multi-source feature interaction learning, named MFSynDCP, aiming to predict the synergistic effects of anti-tumor drug combinations. This model includes a graph aggregation module with an adaptive attention mechanism for learning drug interactions and a multi-source feature interaction learning controller for managing information transfer between different data sources, accommodating both drug and cell line features. Comparative studies with benchmark datasets demonstrate MFSynDCP's superiority over existing methods. Additionally, its adaptive attention mechanism graph aggregation module identifies drug chemical substructures crucial to the synergy mechanism. Overall, MFSynDCP is a robust tool for predicting synergistic drug combinations. The source code is available from GitHub at https://github.com/kkioplkg/MFSynDCP.

Abstract 7125: Inhibition of IDH1 impairs homologous recombination and induces sensitivity to PARP inhibitor in pancreatic cancer

Abstract Introduction: Pancreatic ductal adenocarcinoma (PDAC) is a major cause of cancer-related deaths in the United States. While Poly (ADP-ribose) polymerase (PARP) inhibitors hold promise in treating PDAC with Homologous Recombination Deficiency (HRD). However, HRD occurs in only 10% of cases, leaving 90% with intact Homologous Recombination Proficiency (HRP). Our research highlights the key role of wild-type isocitrate dehydrogenase 1 (IDH1) in maintaining cellular function. We found that inhibiting IDH1 increases DNA damage, suppressing the DNA repair pathway crucial for genomic integrity. This makes cancer cells susceptible to PARP inhibitors, offering a new PDAC treatment approach. Methods: The effect of IDH1 inhibition (Ivosidenib) combined with PARP inhibition (Olaparib) on in vitro growth inhibition were assessed through Trypan blue and PicoGreen in drug combination assays. Additionally, we investigated the molecular mechanism underlying the synergistic effects of drug combination. To further evaluate the therapeutic potential of IDH1 inhibition in combination with PARP inhibitors, we examined tumor growth in xenograft models of PDAC in athymic nude mice. Results: Our data report that, mechanistically, IDH1 inhibition decreased expression of HR DNA repair pathway molecules, such as BRCA1 and BRCA2, and leading to decreased HR repair efficiency in pancreatic cancer cells. Short-term cell viability data demonstrated that targeting IDH1 with ivosidenib when combined with DNA-damaging agent (Olaparib) had a synergistic effect in HRP cell lines (MiaPaCa-2 and Panc-1) with a positive synergy score and Bliss score greater than 1. Additionally, we assessed long-term cell survival using colony formation assays, which yielded a dramatic reduction in cell survival for both Panc1 and MiaPaCa-2 cells when olaparib was combined with ivosidenib, as compared to single-agent controls. Consequently, this reduction increased the levels of apoptosis induced by olaparib. Furthermore, we discovered that these two compounds complemented each other in DNA damage response, leading to an increase in the DNA damage biomarker γ-H2AX. In vivo studies using xenograft models demonstrated that the combination of ivosidenib and olaparib synergistically enhanced anti-tumor activity as compared to either single-agent used alone. Conclusion: Our data provides insights into the mechanisms underlying the susceptibility of PDAC tumor growth to a dual treatment approach involving an IDH1 inhibitor and a PARP inhibitor, particularly in the context of HRP cancer. This highlights the promise of precision medicine for pancreatic cancer treatment, though further pre-clinical studies are needed for validation and refinement. Citation Format: Mehrdad Zarei, Omid Hajihassani, Hallie J. Graor, Alexander W. Loftus, Soubhi Tahhan, Faith Nakazzi, Parnian Naji, Luke D. Rothermel, Jonothan R. Brody, Jordan M. Winter. Inhibition of IDH1 impairs homologous recombination and induces sensitivity to PARP inhibitor in pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7125.

Interpreting drug synergy in breast cancer with deep learning using target-protein inhibition profiles

BackgroundBreast cancer is the most common malignancy among women worldwide. Despite advances in treating breast cancer over the past decades, drug resistance and adverse effects remain challenging. Recent therapeutic progress has shifted toward using drug combinations for better treatment efficiency. However, with a growing number of potential small-molecule cancer inhibitors, in silico strategies to predict pharmacological synergy before experimental trials are required to compensate for time and cost restrictions. Many deep learning models have been previously proposed to predict the synergistic effects of drug combinations with high performance. However, these models heavily relied on a large number of drug chemical structural fingerprints as their main features, which made model interpretation a challenge.ResultsThis study developed a deep neural network model that predicts synergy between small-molecule pairs based on their inhibitory activities against 13 selected key proteins. The synergy prediction model achieved a Pearson correlation coefficient between model predictions and experimental data of 0.63 across five breast cancer cell lines. BT-549 and MCF-7 achieved the highest correlation of 0.67 when considering individual cell lines. Despite achieving a moderate correlation compared to previous deep learning models, our model offers a distinctive advantage in terms of interpretability. Using the inhibitory activities against key protein targets as the main features allowed a straightforward interpretation of the model since the individual features had direct biological meaning. By tracing the synergistic interactions of compounds through their target proteins, we gained insights into the patterns our model recognized as indicative of synergistic effects.ConclusionsThe framework employed in the present study lays the groundwork for future advancements, especially in model interpretation. By combining deep learning techniques and target-specific models, this study shed light on potential patterns of target-protein inhibition profiles that could be exploited in breast cancer treatment.

An End-to-end In-Silico and In-Vitro Drug Repurposing Pipeline for Glioblastoma.

Our study aims to address the challenges in drug development for glioblastoma, a highly aggressive brain cancer with poor prognosis. We propose a computational framework that utilizes machine learning-based propensity score matching to estimate counterfactual treatment effects and predict synergistic effects of drug combinations. Through our in-silico analysis, we identified promising drug candidates and drug combinations that warrant further investigation. To validate these computational findings, we conducted in-vitro experiments on two GBM cell lines, U87 and T98G. The experimental results demonstrated that some of the identified drugs and drug combinations indeed exhibit strong suppressive effects on GBM cell growth. Our end-to-end pipeline showcases the feasibility of integrating computational models with biological experiments to expedite drug repurposing and discovery efforts. By bridging the gap between in-silico analysis and in-vitro validation, we demonstrate the potential of this approach to accelerate the development of novel and effective treatments for glioblastoma.

Effects of baicalin on the proliferation of extranodal NK/T cell lymphoma cells and the sensitivity of the cells to the drug

Purpose: To evaluate the effect of baicalin on the proliferation of extranodal NK/T-cell lymphoma cells and the sensitivity of the cells to the drug. Methods: Cell-counting-kit (CCK) assay was used to determine cell activity, while the antagonistic, superimposed and synergistic effects of drug combinations were investigated by coefficient of drug interaction (CDI). Cell proliferation was assessed using 5-ethynyl-2’- deoxyuridine (EdU) test. Flow cytometry was carried out to determine the apoptosis of studied extranodal NK/T-cell lymphoma cells. Furthermore, western-blot was used to evaluate the expression levels of BCL2, BAX, cleaved-caspase3 and caspase3 of the cells. Results: Survival of SNK-6 and YTS cells decreased gradually in a concentration-dependent manner (r = -0.97 and -0.98). Cell viability at the same concentration diminished with time, but with increase in cisplatin concentration, cell viability at the same time point also decreased. At cisplatin concentrations of 5, 10, and 20 μmol/L, reduction in cell viability between the two groups were significantly different (p < 0.001). Furthermore, the proliferation of SNK-6 and YTS cells decreased with increasing baicalin concentrations while apoptosis of the cells increased with increasing baicalin concentration. Conclusion: Baicalin inhibits the proliferation but promotes the apoptosis of SNK-6 and YTS cells. Cisplatin plus baicalin lowers cell proliferation rate and increase the apoptosis rate of both SNK-6 and YTS cells compared with baicalin or cisplatin alone. This drug combination should be further explored for its apoptosis potential in animal models.

MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders.

Accurate prediction of synergistic effects of drug combinations can reduce the experimental costs for drug development and facilitate the discovery of novel efficacious combination therapies for clinical studies. The drug combinations with high synergy scores are regarded as synergistic ones, while those with moderate or low synergy scores are additive or antagonistic ones. The existing methods usually exploit the synergy data from the aspect of synergistic drug combinations, paying little attention to the additive or antagonistic ones. Also, they usually do not leverage the common patterns of drug combinations across different cell lines. In this paper, we propose a multi-channel graph autoencoder (MGAE)-based method for predicting the synergistic effects of drug combinations (DC), and shortly denote it as MGAE-DC. A MGAE model is built to learn the drug embeddings by considering not only synergistic combinations but also additive and antagonistic ones as three input channels. The later two channels guide the model to explicitly characterize the features of non-synergistic combinations through an encoder-decoder learning process, and thus the drug embeddings become more discriminative between synergistic and non-synergistic combinations. In addition, an attention mechanism is incorporated to fuse each cell-line's drug embeddings across various cell lines, and a common drug embedding is extracted to capture the invariant patterns by developing a set of cell-line shared decoders. The generalization performance of our model is further improved with the invariant patterns. With the cell-line specific and common drug embeddings, our method is extended to predict the synergy scores of drug combinations by a neural network module. Experiments on four benchmark datasets demonstrate that MGAE-DC consistently outperforms the state-of-the-art methods. In-depth literature survey is conducted to find that many drug combinations predicted by MGAE-DC are supported by previous experimental studies. The source code and data are available at https://github.com/yushenshashen/MGAE-DC.

A Combination of Cytokine-Induced Killer Cells With PD-1 Blockade and ALK Inhibitor Showed Substantial Intrinsic Variability Across Non-Small Cell Lung Cancer Cell Lines.

BackgroundCancer heterogeneity poses a serious challenge concerning the toxicity and adverse effects of therapeutic inhibitors, especially when it comes to combinatorial therapies that involve multiple targeted inhibitors. In particular, in non-small cell lung cancer (NSCLC), a number of studies have reported synergistic effects of drug combinations in the preclinical models, while they were only partially successful in the clinical setup, suggesting those alternative clinical strategies (with genetic background and immune response) should be considered. Herein, we investigated the antitumor effect of cytokine-induced killer (CIK) cells in combination with ALK and PD-1 inhibitors in vitro on genetically variable NSCLC cell lines.MethodsWe co-cultured the three genetically different NSCLC cell lines NCI-H2228 (EML4-ALK), A549 (KRAS mutation), and HCC-78 (ROS1 rearrangement) with and without nivolumab (PD-1 inhibitor) and crizotinib (ALK inhibitor). Additionally, we profiled the variability of surface expression multiple immune checkpoints, the concentration of absolute dead cells, intracellular granzyme B on CIK cells using flow cytometry as well as RT-qPCR. ELISA and Western blot were performed to verify the activation of CIK cells.ResultsOur analysis showed that (a) nivolumab significantly weakened PD-1 surface expression on CIK cells without impacting other immune checkpoints or PD-1 mRNA expression, (b) this combination strategy showed an effective response on cell viability, IFN-γ production, and intracellular release of granzyme B in CD3+ CD56+ CIK cells, but solely in NCI-H2228, (c) the intrinsic expression of Fas ligand (FasL) as a T-cell activation marker in CIK cells was upregulated by this additive effect, and (d) nivolumab induced Foxp3 expression in CD4+CD25+ subpopulation of CIK cells significantly increased. Taken together, we could show that CIK cells in combination with crizotinib and nivolumab can enhance the anti-tumor immune response through FasL activation, leading to increased IFN-γ and granzyme B, but only in NCI-H2228 cells with EML4-ALK rearrangement. Therefore, we hypothesize that CIK therapy may be a potential alternative in NSCLC patients harboring EML4-ALK rearrangement, in addition, we support the idea that combination therapies offer significant potential when they are optimized on a patient-by-patient basis.

Abstract PD3-07: Combined inhibition of CDK4/6 and AKT is effective in Rb-intact triple-negative breast cancer of the luminal androgen receptor (LAR) subtype

Abstract Background: Triple-negative breast cancer (TNBC) is a heterogeneous disease group with variable clinico-pathologic features. Based on gene expression profiles, TNBCs are grouped into 6 major subtypes. The Luminal androgen receptor (LAR) subtype is enriched for potentially targetable biomarkers, including high androgen receptor (AR) expression, high rates of PIK3CA mutations, and intact Rb. The purpose of this study was to investigate the most effective combinations of CDK4/6, AR, and PI3K-AKT inhibitors in pre-clinical models of LAR TNBC for future clinical trial design. Methods: MDA-MB-453 and MFM-223 (both Rb-intact/PTEN-intact/PIK3CA-mutant) and CAL-148 (Rb-null/PTEN-null/PIK3CA-mutant) LAR TNBC cell lines were treated with the CDK4/6 inhibitor palbociclib, the PI3Kα inhibitor alpelisib, the AKT inhibitor capivasertib, and the AR antagonist enzalutamide, each alone or in different combinations. Drug sensitivity was determined by coulter counter cell counts in 2D, colony formation, and the CellTiterGlo cell viability assay. The combination index (CI) which defines synergism (CI < 1), additive effect (CI = 1) and antagonism (CI > 1), calculated by the CompuSyn method, was used to evaluate the synergistic effects of drug combinations. Expression of cell cycle and PI3K-AKT downstream signaling molecules was measured by western blot analysis. An androgen response element (ARE) luciferase-based reporter assay was used to evaluate AR transcriptional activity. Results: Rb-intact LAR TNBC cell lines were sensitive to single-agent palbociclib, alpelisib or capivasertib (IC50, ~500 nM). Enzalutamide had minimal growth inhibitory activity (IC50, 15-25 μM). Palbociclib combined with either alpelisib or capivasertib synergistically inhibited proliferation of LAR TNBC cells (CI values, 0.07-0.86). Treatment of Rb-intact LAR TNBC cells with palbociclib monotherapy suppressed Rb phosphorylation and resulted in adaptive phosphorylation/activation of S473 AKT and AKT substrates GSKβ and PRAS40 at 24h. These responses were not observed in Rb-null CAL-148 cells. Palbociclib-induced phosphorylation of AKT substrates as well as induction P-S6 and P-4EBP1 were better suppressed by capivasertib than by alpelisib over a dose range. Addition of the PI3Kβ/δ inhibitor AZD8186 to alpelisib markedly enhanced the inhibition of P-AKT, P-PRAS40 and P-Sin, suggesting inhibition of PI3Kα is inadequate to block the adaptive response to palbociclib in these cells. Mean CI values showed that the combination of palbociclib/capivasertib was more synergistic against LAR TNBC cells compared to palbociclib/alpelisib (mean CI, 0.29 vs. 0.78). ARE reporter activity did not change upon inhibition of PI3K or AKT with alpelisib or capivasertib, respectively. Conclusions: Our results suggest that addition of an AKT inhibitor to palbociclib suppresses the rebound activation of AKT following treatment with the CDK4/6i and is effective in LAR TNBC with wild type Rb. In vivo studies are underway to investigate the antitumor activity of the combination of palbociclib and capivasertib in LAR TNBC xenografts. Citation Format: Gun Min Kim, Kyung-min Lee, Dhivya Sudhan, Albert Lin, Arnaldo Marin, Sumanta Chatterjee, Dan Ye, Vishal Kandagatla, Saurabh Mendiratta, Ariella Hanker, Carlos Arteaga. Combined inhibition of CDK4/6 and AKT is effective in Rb-intact triple-negative breast cancer of the luminal androgen receptor (LAR) subtype [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD3-07.

Dose Optimization of Aditoprim-Sulfamethoxazole Combinations Against Trueperella pyogenes From Patients With Clinical Endometritis by Using Semi-mechanistic PK/PD Model.

Combinations of two and more drugs with different target sites are being used as a new treatment regimen for resistant clones of bacteria. Though, achieving the right combination of the drugs for optimal dosage regimen is challenging. In our study, we studied the antimicrobial effect of aditoprim, a novel dihydrofolate reductase inhibitor, and its synergistic effect with sulfamethoxazole. Synergy testing was performed by checkerboard micro dilution method and validation of different checkerboard ratios by static and dynamic time-kill analysis and in vitro pharmacokinetic/pharmacodynamics (PK/PD) model, and semi mechanistic PK/PD modeling was used to calculate and validate the synergistic effect of drug combination. Both checkerboard and static time-kill assays demonstrated the greater synergistic effect [fractional inhibitory concentration index (FICI) = 0.37] of the aditoprim [minimum inhibitory concentration (MIC) = 0.25 µg/ml]-sulfamethoxazole (MIC=>64 µg/ml) combination against all T. Pyogenes isolates. In the in vitro PK/PD model, the dosage proportion of sulfamethoxazole 4 mg/ml twice a day in combination with steady-state aditoprim 1 mg/ml efficiently repressed the growth of bacteria in 24 h with the ratio of 2-log10 decrease, related to the early inoculum against three T. Pyogenes isolates. The semi mechanistic PK/PD model projected that a combination of a high dose of aditoprim (2 mg/ml) with sulfamethoxazole (2 mg/day) was necessary to attain the killing of bacteria below the detection limit (limit of detection (LOD); i.e., 1 log10 CFU/ml) at 24 h with an MIC sulfamethoxazole (SMZ) of 64 µg/ml. However, it is anticipated that a combination of high dose of aditoprim with sulfamethoxazole is critical to attain the suppressed bacterial growth to < LOD. This study represents essential PK/PD modeling for optimization of combination of aditoprim and sulfamethoxazole to suppress growth of T. Pyogenens.

Anticancer effects of the combined Thai noni juice ethanolic extracts and 5-fluorouracil against cholangiocarcinoma cells in vitro and in vivo

Application of 5-fluorouracil (5-FU) in cholangiocarcinoma (CCA) is limited by adverse side effects and chemoresistance. Therefore, the combination therapy of 5-FU with other substances, especially natural products may provide a new strategy for CCA treatment. The aim of this study was to evaluate the combination effects of 5-FU and two ethanolic extracts of Thai noni juice (TNJ) products on CCA cell lines and nude mice xenografts. The results of antiproliferative assay showed the combination treatment of 5-FU and each TNJ ethanolic extract exerted more cytotoxicity on CCA cells than either single agent treatment. Synergistic effects of drug combinations can enable the dose reduction of 5-FU. The mechanism underlying a combination treatment was apoptosis induction through an activation of p53 and Bax proteins. In the nude mouse xenograft model, combination treatments of 5-FU with each TNJ ethanolic extract suppressed the growth of CCA cells implanted mice more than single agent treatments with no effects on mouse body weight, kidney, and spleen. Moreover, low doses of TNJ ethanolic extracts reduced the hepatotoxicity of 5-FU in nude mice. Taken together, these data suggested that the ethanolic extracts of TNJ products can enhance the anti-CCA effect and reduce toxicity of 5-FU.

Intra-Articular Dual Drug Delivery for Synergistic Rheumatoid Arthritis Treatment.

Systemic rheumatoid arthritis (RA) regimens fail to attain effective drug level at the affected joints and are associated with serious side effects. Herein, an attempt made to improve therapeutic outcomes of both leflunomide (LEF) which is a disease modifying antirheumatic and dexamethasone (Dex) through local delivery of combination therapy by intra-articular route. LEF and Dex were encapsulated in nanostructured lipid carriers (NLCs) and PLGA nanoparticles (NPs), respectively. Both nanocarriers were loaded into chitosan/β glycerophosphate (CS/βGP) thermo-sensitive hydrogels and injected intra-articularly in adjuvant induced RA rat model. Particle size of LEF NLCs and selected Dex NPs formulations were 200 and 119nm, respectively. Dex NPs and LEF NLCs showed a sustained release profile for up to 58 and 17 days, respectively. After 14 days of treatment remarkable joint healing was observed for groups treated with Dex NPs in combination with either free LEF or LEF NLCs in CS/βGP hydrogel. Joint diameter measurements, TNF α levels and histopathological examination of dissected joints showed comparable values to the negative control group. This might be attributed to the synergistic effect of drug combination besides the ability of nanocarriers loaded hydrogel to prolong joint residence time and enhance joint healing potential.

Abstract PS4-39: Efficacy of combined CDK4/6 inhibitor and PARP inhibitor in the treatment of BRCA1 mutant triple negative breast cancer

Abstract Background: PARP inhibitors (PARPi) benefit only a fraction of breast cancer with BRCA mutation, and are even more limited in triple negative breast cancer (TNBC), due to clinical primary and acquired resistance. Here we found that the efficacy of PARPi in TNBC can be expanded with CDK4/6 inhibitors (CDK4/6i). Methods: We screened sensitive and resistant PARPi cell lines from existing TNBC cell lines and cultured PARPi-resistant strains by gradient concentration increase method. The effects of PARP inhibitor Olaparib and CDK4/6 inhibitor Palbociclib on TNBC cells lines were examined in vitro and in vivo. Combination index (CI) was used to evaluate the synergistic effects of drug combination. Results: We screened out the cell line MDA-MB-436, which was the most sensitive to PARPi, and the cell lines HCC1937 and SUM149, which were relatively resistant to PARPi, all of which were BRCA1 mutants. We demonstrated for the first time that the combination of PARPi and CDK4/6i has synergistic effects against some TNBCs both in vitro and in vivo, and was verified by CI&amp;lt;0.9. Further experiments confirmed that PARPi combined with CDK4/6i inhibited cell proliferation and migration, and increased apoptosis and DNA damage. In the PARPi sensitive BRCA-/TNBC cell (MDA-MB-436), the inhibitory effect of monotherapy PARPi was obvious; In the PARPi resistant BRCA-/TNBC cells (HCC1937 and SUM149), CDK4/6i was added to achieve significant growth inhibition. In the timing of medication, PARPi followed by CDK4/6i had better inhibitory effect.Conclusions: In some BRCA-/ TNBCs, PARPi combined with CDK4/6i had a synergistic effect. Even in PARPi-resistant cells, combined treatment could enhance the efficacy and might reverse the drug resistance to some extent. The combination index index of HCC1937 at different drug concentrations (uM)The combination index index of HCC1937 at different drug concentrations (uM)OlaparibPalbociclib25.0016.6711.117.414.943.292.191.460.980.65P-25.000.459110.668320.866540.869640.993450.989890.97841.003221.073741.00809P-20.000.537090.851131.022651.149561.231561.270271.189071.145021.14811.25795P-15.000.594121.018141.134481.056960.928140.955670.889760.842040.81060.81348P-10.000.475540.860580.77190.717480.62330.615880.561390.52960.510250.49752P-5.000.509450.762230.746560.683340.56030.499410.467390.41490.390670.37502P-2.500.573130.706410.714750.661230.519270.455960.350980.29340.319310.2756 Citation Format: Xiuzhi Zhu, Li Chen, Zhonghua Wang. Efficacy of combined CDK4/6 inhibitor and PARP inhibitor in the treatment of BRCA1 mutant triple negative breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS4-39.

Synergistic drug combination prediction in multi-input neural network

Synergistic effects of drug combinations are very important in improving drug efficacy or reducing drug toxicity. However, due to the complex mechanism of action between drugs, it is expensive to screen new drug combinations through trials. It is well known that virtual screening of computational models can effectively reduce the test cost. Recently, foreign scholars successfully predicted the synergistic value of new drug combinations on cancer cell lines by using deep learning model DeepSynergy. However, DeepSynergy is a two-stage method and uses only one kind of feature as input. In this study, we proposed a new end-to-end deep learning model, MulinputSynergy which predicted the synergistic value of drug combinations by integrating gene expression, gene mutation, gene copy number characteristics of cancer cells and anticancer drug chemistry characteristics. In order to solve the problem of high dimension of features, we used convolutional neural network to reduce the dimension of gene features. Experimental results showed that the proposed model was superior to DeepSynergy deep learning model, with the mean square error decreasing from 197 to 176, the mean absolute error decreasing from 9.48 to 8.77, and the decision coefficient increasing from 0.53 to 0.58. This model could learn the potential relationship between anticancer drugs and cell lines from a variety of characteristics and locate the effective drug combinations quickly and accurately.

Kinase inhibitor library screening identifies synergistic drug combinations effective in sensitive and resistant melanoma cells

BackgroundMelanoma is the most aggressive and deadly form of skin cancer with increasing case numbers worldwide. The development of inhibitors targeting mutated BRAF (found in around 60% of melanoma patients) has markedly improved overall survival of patients with late-stage tumors, even more so when combined with MEK inhibitors targeting the same signaling pathway. However, invariably patients become resistant to this targeted therapy resulting in rapid progression with treatment-refractory disease. The purpose of this study was the identification of new kinase inhibitors that do not lead to the development of resistance in combination with BRAF inhibitors (BRAFi), or that could be of clinical benefit as a 2nd line treatment for late-stage melanoma patients that have already developed resistance.MethodsWe have screened a 274-compound kinase inhibitor library in 3 BRAF mutant melanoma cell lines (each one sensitive or made resistant to 2 distinct BRAFi). The screening results were validated by dose-response studies and confirmed the killing efficacies of many kinase inhibitors. Two different tools were applied to investigate and quantify potential synergistic effects of drug combinations: the Chou-Talalay method and the Synergyfinder application. In order to exclude that resistance to the new treatments might occur at later time points, synergistic combinations were administered to fluorescently labelled parental and resistant cells over a period of > 10 weeks.ResultsEight inhibitors targeting Wee1, Checkpoint kinase 1/2, Aurora kinase, MEK, Polo-like kinase, PI3K and Focal adhesion kinase killed melanoma cells synergistically when combined with a BRAFi. Additionally, combination of a Wee1 and Chk inhibitor showed synergistic killing effects not only on sensitive cell lines, but also on intrinsically BRAFi- and treatment induced-resistant melanoma cells. First in vivo studies confirmed these observations. Interestingly, continuous treatment with several of these drugs, alone or in combination, did not lead to emergence of resistance.ConclusionsHere, we have identified new, previously unexplored (in the framework of BRAFi resistance) inhibitors that have an effect not only on sensitive but also on BRAFi-resistant cells. These promising combinations together with the new immunotherapies could be an important step towards improved 1st and 2nd line treatments for late-stage melanoma patients.

SUSTAINED RELEASE TABLETS OF SORAFENIB-SILIBININ COMBINATIONS FOR THE TREATMENT OF HEPATOCELLULAR CARCINOMA

Objective: The aim of this study was to develop polymer coated sustained release tablet using sorafenib and silibinin combination for the treatment of hepatocellular carcinoma.Methods: The qualitative analysis such as weight variation, friability, hardness, interaction studies, disintegration and in vitro release were performed to validate formulated tablets. We have maintained the acceptable official limits for weight variation, friability, hardness and disintegration time according to prescribed pharmacopoeial recommendation. In vitro drug release studies were performed using USP-II (paddle type) dissolution apparatus. The MTT assay was performed for assessment of Cell viability of drug combination for tablet formulation. Molecular docking studies have been performed to determine the combinatorial mode of action for the tablet formulation.Results: Friability and weight variation were less than 1% for each formulation, which were within range of prescribed pharmacopoeial recommendation. The hardness of 20 tablets showed 5-6.5Kg/cm2 for all formulations 5-6.5Kg/cm2. The optimized formulation resulted in 98% drug release after 28 h. The present study reports the synergistic effects of drug combination to inhibit cell growth in HepG2 cell line. Molecular docking studies showed that sorafenib has high binding affinity for B-Raf vascular endothelial growth factor receptor β and protein kinase B. Silibinin showed binding affinity with MAP kinase-11, protein phosphatase 2 A and tankyrase.Conclusion: The present study reports for the first time a novel formulation for sustained release and reduced toxicity of sorafenib with enhanced inhibitory effect of the drug combination on cancerous hepatic cell line as well collaborative mechanism of action for the formulation.

Predict effective drug combination by deep belief network and ontology fingerprints.

The synergistic effect of drug combination is one of the most desirable properties for treating cancer. However, systematically predicting effective drug combination is a significant challenge. We report here a novel method based on deep belief network to predict drug synergy from gene expression, pathway and the Ontology Fingerprints-a literature derived ontological profile of genes. Using data sets provided by 2015 DREAM competition, our analysis shows that this integrative method outperforms published results from the DREAM website for 4999 drug pairs, demonstrating the feasibility of predicting drug synergy from literature and the -omics data using advanced artificial intelligence approach.

Evaluation of Drug Combination Effect Using a Bliss Independence Dose–Response Surface Model

ABSTRACTTo test the anticancer effect of combining two drugs targeting different biological pathways, the popular way to show synergistic effect of drug combination is a heat map or surface plot based on the percent excess the Bliss prediction using the average response measures at each combination dose. Such graphs, however, are inefficient in the drug screening process and it does not give a statistical inference on synergistic effect. To make a statistically rigorous and robust conclusion for drug combination effect, we present a two-stage Bliss independence response surface model to estimate an overall interaction index (τ) with 95% confidence interval (CI). By taking into all data points account, the overall τ with 95% CI can be applied to determine if the drug combination effect is synergistic overall. Using some example data, the two-stage model was compared to a couple of classic models following Bliss rule. The data analysis results obtained from our model reflect the pattern shown from other models. The application of overall τ helps investigators to make decision easier and accelerate the preclinical drug screening.

Antiprotozoal treatment of canine babesiosis

Canine babesiosis is a tick-borne disease caused by several Babesia spp. which have different susceptebility to anti-protozoal drugs. A few drugs and drug combinations are used in the treatment of canine babesiosis often without complete parasite elimination leaving treated dogs as carriers which could relapse with clinical disease and also transmit infection further. Although the large form canine babesial species Babesia canis, Babesia vogeli and Babesia rossi are sensitive to the aromatic diamidines imidocarb dipropionate and diminazene aceturate, small form species such as Babesia gibsoni, Babesia conradae and Babesia vulpes (Theileria annae) are relatively resistant to these drugs and are treated with the combination of the hydroxynaphthoquinone atovaquone and the antibiotic azithromycin. Azithromycin and other antibiotics that have anti-protozoal properties target the apicoplast, a relict plastid found in protozoa, and exert a delayed death effect. The triple combination of clindamycin, diminazene aceturate and imidocarb dipropionate is also effective against B. gibsoni and used to treat atovaquone-resistant strains of this species. Novel drugs and the synergistic effects of drug combinations against Babesia infection should be explored further to find new treatments for canine babesiosis.

The Prediction of Drug-Disease Correlation Based on Gene Expression Data.

The explosive growth of high-throughput experimental methods and resulting data yields both opportunity and challenge for selecting the correct drug to treat both a specific patient and their individual disease. Ideally, it would be useful and efficient if computational approaches could be applied to help achieve optimal drug-patient-disease matching but current efforts have met with limited success. Current approaches have primarily utilized the measureable effect of a specific drug on target tissue or cell lines to identify the potential biological effect of such treatment. While these efforts have met with some level of success, there exists much opportunity for improvement. This specifically follows the observation that, for many diseases in light of actual patient response, there is increasing need for treatment with combinations of drugs rather than single drug therapies. Only a few previous studies have yielded computational approaches for predicting the synergy of drug combinations by analyzing high-throughput molecular datasets. However, these computational approaches focused on the characteristics of the drug itself, without fully accounting for disease factors. Here, we propose an algorithm to specifically predict synergistic effects of drug combinations on various diseases, by integrating the data characteristics of disease-related gene expression profiles with drug-treated gene expression profiles. We have demonstrated utility through its application to transcriptome data, including microarray and RNASeq data, and the drug-disease prediction results were validated using existing publications and drug databases. It is also applicable to other quantitative profiling data such as proteomics data. We also provide an interactive web interface to allow our Prediction of Drug-Disease method to be readily applied to user data. While our studies represent a preliminary exploration of this critical problem, we believe that the algorithm can provide the basis for further refinement towards addressing a large clinical need.

Developing an Agent-Based Drug Model to Investigate the Synergistic Effects of Drug Combinations.

The growth and survival of cancer cells are greatly related to their surrounding microenvironment. To understand the regulation under the impact of anti-cancer drugs and their synergistic effects, we have developed a multiscale agent-based model that can investigate the synergistic effects of drug combinations with three innovations. First, it explores the synergistic effects of drug combinations in a huge dose combinational space at the cell line level. Second, it can simulate the interaction between cells and their microenvironment. Third, it employs both local and global optimization algorithms to train the key parameters and validate the predictive power of the model by using experimental data. The research results indicate that our multicellular system can not only describe the interactions between the microenvironment and cells in detail, but also predict the synergistic effects of drug combinations.