Efficacy Of Drug Combinations Research Articles

DDDR-30. ENHANCED CHEMO AND RADIO-SENSITIZATION OF GLIOMAS BY HSP90 INHIBITION WITH JIN-001, A NOVEL BLOOD-BRAIN BARRIER PENETRANT HSP90 INHIBITOR

Abstract BACKGROUND HSP90, an abundant molecular chaperone, plays a pivotal role as a key regulator in the growth and survival of malignant cells and has been a therapeutic target in various cancers. We have previously reported a strong role for HSP90 inhibitors to disable DNA damage repair mechanisms and induce cell death. Here, we determined the effects of JIN-001 mediated HSP90 inhibition on alkylator and radiation induced DNA damage. Method: Using a panel of patient-derived glioma stem cells (GSCs) we determined the synergy scores for cell viability of JIN-001 (0.1µM to 0.5µM) in combination with radiation (RT) and Temozolomide (TMZ). We also examined cell cycle dynamics and cell death of the combination using flow cytometry (PI and PI/Annexin method) and cell viability assays. Additionally, organotypic human glioma slice cultures treated with JIN-001 and chemoradiation were analyzed for changes in gene and protein expression as well as cell viability in situ. Ongoing in-vivo studies are being conducted to further elucidate drug combination efficacy in mouse models. RESULTS JIN-001 sensitized the cells to chemoradiation leading to cell cycle arrest and cell death which was higher in the combination compared to single agent. Significant changes in expression of downstream molecules of homologous recombination pathway was noted (ATM, pATM, CHK1, pCHK1, ATR, pATR) in the cell lines and confirmed in human glioma slice culture. Based on the synergy score from four cell lines, an effective dose for combination of JIN-001+RT+TMZ was identified; Results of ongoing ex-vivo human glioma gene expression changes and in-vivo mouse studies will be presented. CONCLUSION Through a comprehensive analysis, we demonstrated that HSP90 inhibitor JIN-001 sensitizes glioma cells to radio-chemotherapy including in human glioma tissue. Our study underscores the potential of combining JIN-001 with standard therapies in glioma treatment in future clinical trials for patients with glioblastoma.

Exploiting acquired vulnerability to develop novel treatments for cholangiocarcinoma.

Cholangiocarcinoma (CCA) presents a formidable therapeutic challenge due to its extensive heterogeneity and plasticity, which inevitably lead to acquired resistance to current treatments. However, recent evidence suggests that acquired drug resistance is associated with a fitness cost resulting from the myriad of acquired alterations under the selective pressure of the primary treatment. Consequently, CCA patients with acquired resistance are more susceptible to alternative therapies that are ineffective as monotherapies. This phenomenon, termed "acquired vulnerability," has garnered significant interest in drug development, as the acquired alterations could potentially be exploited therapeutically. This review elucidates the modes of acquired vulnerability, methods for identifying and exploiting acquired vulnerabilities in cancer (particularly in CCA), and strategies to enhance the clinical efficacy of drug combinations by leveraging the principle of acquired vulnerability. Identifying acquired vulnerabilities may pave the way for novel drug combinations to effectively treat highly heterogeneous and adaptable malignancies such as CCA.

Modeling transmission mechanism to infer treatment efficacy of different drugs and combination therapy against Trichuris trichiura

Trichuris trichiura is one of four soil-transmitted helminth species that, collectively, are responsible for a considerable public health burden. The World Health Organization recommends preventive chemotherapy as the main intervention to eliminate soil-transmitted helminthiasis as a public health problem. Clinical trials estimated the efficacy of different drugs and treatment regimen against T. trichiura and other soil-transmitted helminth species, whilst meta-analyses and modeling efforts were conducted to determine the most efficacious drugs and drug combinations. Of note, the diagnostic error was often neglected, and hence, cure rates (CRs) might be overestimated. We developed a Bayesian model, which estimates drug efficacy against T. trichiura, taking into account the transmission mechanism and the diagnostic error. The model was fitted to individual-level egg count data from an ensemble of seven trials with 29 treatments. We estimated the ‘true’ CRs, which were consistently lower than those reported in the literature. In our analysis, the treatment with the highest CR was combination therapy of albendazole plus pyrantel pamoate plus oxantel pamoate with a CR of 79% and an egg reduction rate (ERR) of 91%. Albendazole plus oxantel pamoate showed the highest ERR of 97% and a CR of 69%. Additionally, we estimated the intensity-dependent sensitivity of the Kato-Katz technique. For 24 eggs per gram of stool, the sensitivity was around 50% for a single Kato-Katz thick smear and increased to almost 70% for duplicate Kato-Katz thick smears. Combination therapies against soil-transmitted helminthiasis should be considered and the evaluation of infection intensity in low transmission settings via multiple Kato-Katz thick smears is recommended.

Co-delivery of protopanaxatriol/icariin into niche cells restores bone marrow niches to rejuvenate HSCs for chemotherapy-induced myelosuppression

BackgroundUp to 80 % of chemotherapeutic drugs induce myelosuppression in patients. Chemotherapy not only impairs of hematopoietic stem cells (HSCs) but also damages bone marrow niches (vascular and endosteal). Current treatments for myelosuppression overlook these chemotherapy-induced damages to bone marrow niches and the critical role of niche restoration on hematopoietic regeneration. Ginsenoside protopanaxatriol (PPT) protects vascular endothelium from injury, while icariin (ICA) promotes osteogenic differentiation. The combination of PPT and ICA aims to restore damaged vascular and endosteal niches, thus rejuvenating HSCs for treating myelosuppression. PurposeThis study aims to develop effective, bone marrow niche-directed PPT/ICA therapies for treating chemotherapy-induced myelosuppression. Methods3D cell spheroids were used to investigate the effects of PPT/ICA on cell-cell interactions in vascular niches, osteogenesis, and extracellular matrix (ECM) secretion in endosteal niches. In vitro mimic niche models were designed to access the drug combination's efficacy in rejuvenating and mobilizing in HSCs within bone marrow niches. The delivery capability of PPT/ICA to key niche cell types (mesenchymal stromal cells (MSCs), endothelial cells (ECs), and osteoblasts (OBs)) via nanocarriers has been determined. DSS6 peptide-modified nanoparticles (DSS6-NPs) were prepared for specific co-delivery of PPT/ICA into key niche cell populations in vivo. ResultsPPT can prevent vascular niche injury by restoring vascular EC cell-cell adhesion and the intercellular interactions between ECs and MSCs in 5-fluorouracil (5-FU)-damaged cell spheroids. ICA repaired 5-FU-damaged endosteal niches by promoting osteogenesis and ECM secretion. The combination of PPT and ICA restores key HSC niche factor gene expressions, normalizing HSC differentiation and mobilization. The in vitro cellular uptake efficiency of nanocarriers in a mimic niche is positively correlated with their in vivo delivery into bone marrow niche cells. DSS6-NPs greatly enhance the delivery of PPT/ICA into MSCs and OBs within bone marrow niches. Co-loading of PPT/ICA into DSS6-NPs effectively repairs damaged bone marrow niches and promotes HSC rejuvenation in vivo. ConclusionThe combination of PPT and ICA effectively prevents injury to the vascular and endosteal niches, thereby promoting hematopoietic regeneration in the bone marrow. This study provides novel niche-directed PPT/ICA therapies for managing chemotherapy-induced myelosuppression.

Exploring potential therapeutic combinations for castration-sensitive prostate cancer using supercomputers: a proof of concept study

To address the challenge of finding new combination therapies against castration-sensitive prostate cancer, we introduce Vini, a computational tool that predicts the efficacy of drug combinations at the intracellular level by integrating data from the KEGG, DrugBank, Pubchem, Protein Data Bank, Uniprot, NCI-60 and COSMIC databases. Vini is a computational tool that predicts the efficacy of drugs and their combinations at the intracellular level. It addresses the problem comprehensively by considering all known target genes, proteins and small molecules and their mutual interactions involved in the onset and development of cancer. The results obtained point to new, previously unexplored combination therapies that could theoretically be promising candidates for the treatment of castration-sensitive prostate cancer and could prevent the inevitable progression of the cancer to the incurable castration-resistant stage. Furthermore, after analyzing the obtained triple combinations of drugs and their targets, the most common targets became clear: ALK, BCL-2, mTOR, DNA and androgen axis. These results may help to define future therapies against castration-sensitive prostate cancer. The use of the Vini computer model to explore therapeutic combinations represents an innovative approach in the search for effective treatments for castration-sensitive prostate cancer, which, if clinically validated, could potentially lead to new breakthrough therapies.

Optimization and validation of a fat-on-a-chip model for non-invasive therapeutic drug discovery.

Obesity is a significant public health concern that is closely associated with various comorbidities such as heart disease, stroke, type II diabetes (T2D), and certain cancers. Due to the central role of adipose tissue in many disease etiologies and the pervasive nature in the body, engineered adipose tissue models are essential for drug discovery and studying disease progression. This study validates a fat-on-a-chip (FOAC) model derived from primary mature adipocytes. Our FOAC model uses a Micronit perfusion device and introduces a novel approach for collecting continuous data by using two non-invasive readout techniques, resazurin and glucose uptake. The Micronit platform proved to be a reproducible model that can effectively maintain adipocyte viability, metabolic activity, and basic functionality, and is capable of mimicking physiologically relevant responses such as adipocyte hypertrophy and insulin-mediated glucose uptake. Importantly, we demonstrate that adipocyte size is highly dependent on extracellular matrix properties, as adipocytes derived from different patients with variable starting lipid areas equilibrate to the same size in the hyaluronic acid hydrogel. This model can be used to study T2D and monitor adipocyte responses to insulin for longitudinally tracking therapeutic efficacy of novel drugs or drug combinations.

Network-driven cancer cell avatars for combination discovery and biomarker identification for DNA damage response inhibitors

Combination therapy is well established as a key intervention strategy for cancer treatment, with the potential to overcome monotherapy resistance and deliver a more durable efficacy. However, given the scale of unexplored potential target space and the resulting combinatorial explosion, identifying efficacious drug combinations is a critical unmet need that is still evolving. In this paper, we demonstrate a network biology-driven, simulation-based solution, the Simulated Cell™. Integration of omics data with a curated signaling network enables the accurate and interpretable prediction of 66,348 combination-cell line pairs obtained from a large-scale combinatorial drug sensitivity screen of 684 combinations across 97 cancer cell lines (BAC = 0.62, AUC = 0.7). We highlight drug combination pairs that interact with DNA Damage Response pathways and are predicted to be synergistic, and deep network insight to identify biomarkers driving combination synergy. We demonstrate that the cancer cell ‘avatars’ capture the biological complexity of their in vitro counterparts, enabling the identification of pathway-level mechanisms of combination benefit to guide clinical translatability.

The combination therapy using tyrosine kinase receptors inhibitors and repurposed drugs to target patient-derived glioblastoma stem cells

The lesson from many studies investigating the efficacy of targeted therapy in glioblastoma (GBM) showed that a future perspective should be focused on combining multiple target treatments. Our research aimed to assess the efficacy of drug combinations against glioblastoma stem cells (GSCs). Patient-derived cells U3042, U3009, and U3039 were obtained from the Human Glioblastoma Cell Culture resource. Additionally, the study was conducted on a GBM commercial U251 cell line. Gene expression analysis related to receptor tyrosine kinases (RTKs), stem cell markers and genes associated with significant molecular targets was performed, and selected proteins encoded by these genes were assessed using the immunofluorescence and flow cytometry methods. The cytotoxicity studies were preceded by analyzing the expression of specific proteins that serve as targets for selected drugs. The cytotoxicity study using the MTS assay was conducted to evaluate the effects of selected drugs/candidates in monotherapy and combinations. The most cytotoxic compounds for U3042 cells were Disulfiram combined with Copper gluconate (DSF/Cu), Dacomitinib, and Foretinib with IC50 values of 52.37 nM, 4.38 µM, and 4.54 µM after 24 h incubation, respectively. Interactions were assessed using SynergyFinder Plus software. The analysis enabled the identification of the most effective drug combinations against patient-derived GSCs. Our findings indicate that the most promising drug combinations are Dacomitinib and Foretinib, Dacomitinib and DSF/Cu, and Foretinib and AZD3759. Since most tested combinations have not been previously examined against glioblastoma stem-like cells, these results can shed new light on designing the therapeutic approach to target the GSC population.

Efficacy and safety of various drug combinations in treating plaque Psoriasis: A meta-analysis

Background Psoriasis, a chronic inflammatory disease affecting the skin, joints, and nails (2-3% worldwide), significantly affects quality of life. Genetic and environmental factors also play key roles. Topical corticosteroids, calcineurin inhibitors, and oral corticosteroids help to manage plaque psoriasis symptoms, but combination therapies might offer greater effectiveness and improved safety profiles. These combinations could potentially reduce medication dosages and side effects in patients. Objective To assess the efficacy and safety of various drug combinations over conventional monotherapies in the treatment of moderate to severe plaque psoriasis, which incorporates palmoplantar psoriasis and psoriasis vulgaris, by discovering and utilizing research articles comprising the same or similar variables as PASI 75 and evaluating the information using RevMan v5.4.1. Method We reviewed the efficacy and safety of combination therapy vs. monotherapy/placebo for moderate-to-severe plaque psoriasis (palmoplantar and vulgaris) using PASI 75 via RevMan v5.4.1. Risk of bias assessment and funnel plots were employed to assess the heterogeneity of each paper. Inclusion criteria – Publications < 20yrs, RCTs, plaque/palmoplantar/psoriasis vulgaris; exclusion criteria – Guttate/arthritic psoriasis, pediatric and pregnant individuals, publications > 20 yrs. Results Seventeen studies were analyzed, comprising a total of 2291 patients (n=1147 with combination regimens and n=1144 with control regimen in the analysis). A significant PASI 75- response was observed in the pioglitazone combination subgroup as compared to placebo (OR=4.92,95% CI 2.19-11.05, P = 0.0001); methotrexate combination subgroup as compared to placebo (OR=2.56, 95% CI 1.67-3.94, P< 0.0001) test of subgroup differences showed P= 0.14, I2= 34%. Incidence rate of abnormality in levels of liver enzymes (OR=1.89,9.5% CI 6.69-5.22, P=0.22), nausea (OR=1.28,95% CI 0.77-2.14, P=0.34), headache (OR=1.28,95% CI 0.77-2.14, P=0.58), fatigue (OR=0.89, 95% CI 0.41-1.90, P=0.45).] Conclusion This study showed that combination therapy is very effective for plaque psoriasis, with promising combinations of pioglitazone. While safety seems similar between the groups, larger studies are needed to determine the long-term effects. These findings suggest that personalized treatment plans could improve outcomes; however, confirmation through larger trials is crucial before wider use. This opens doors to research on optimal combinations for individual patients.

Abstract 6625: Development of novel mixed-cell models to capture heterogeneity in castration-resistant prostate cancer

Abstract Heterogeneity is ubiquitous in cancer and treatment responses among patient cohorts are highly variable, leading to drug resistance and disease relapse. Yet, preclinical drug screening is usually performed in cell lines or patient cells, one at a time, without consideration of cancer heterogeneity. Consequentially, positive outcomes obtained in the preclinical stage do not translate well into subsequent clinical successes. Castration-resistant prostate cancer (CRPC) is a highly lethal form of prostate cancer (PC) exhibiting a high degree of genetic and phenotypic heterogeneity with very few therapeutic options. Therefore, we created mixed-cell models depicting heterogeneity in various clinical contexts, where multiple PC cell lines representative of distinct CRPC tumor variants were labeled and combined in co-cultures to mimic the complex molecular landscape of CRPC patient cohorts. Specifically, transcriptomic profiles of PC cell lines were clustered with patient tumor specimens to identify cell lines that are genetically similar to distinct patient clusters representing CRPC tumor subtypes. Since heterogeneity has been linked to the clinical efficacy of combinatorial therapies, we applied these models to evaluate drug combinations. A combination of docetaxel and dasatinib that was previously assessed in a PC trial, was tested in the mixed-cell model and experimental findings concurred with the published clinical results, demonstrating the utility and accuracy of the model. Additionally, a computational model, IDACombo, was employed to nominate novel potentially efficacious drug combinations using monotherapy response data of cancer cell lines, a number of which were successfully validated in the mixed-cell model. More importantly, we demonstrated that while individual cell line screens may result in erroneous conclusions, combination efficacy can be accurately measured in a heterogeneous cell pool emulating a diverse patient cohort. We further explored phenotypic heterogeneity in advanced disease states, by mixing parent and standard-of-care (SOC)-resistant CRPC clonal variants. Drugs with selective potency in the resistant (vs. the parent) clone were computational prioritized and when combined with the SOC and tested in the mixed-cell models, outperformed either monotherapy. Additionally, we investigated the effects of altering the relative proportion of the constituent clones to mimic different stages of acquired therapy resistance. Furthermore, we generated mixed-cell xenograft models by subcutaneously injecting the labeled cell-line mixtures in mice and characterized the harvested tumors to detect and quantify the individual subpopulations. Development of these preclinical models holds enormous promise not only for advancing novel therapeutic strategies for cancer, but also for providing insights into the mechanisms of resistance and recurrence. Citation Format: Sampreeti Jena, Daniel Kim, Adam M. Lee, Weijie Zhang, Scott Dehm, R. Stephanie Huang. Development of novel mixed-cell models to capture heterogeneity in castration-resistant prostate 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 6625.

Abstract 6492: KRAS inhibitor-based combinations in pancreatic cancer identified through Optim.AI

Abstract PURPOSE: KRAS is the most frequently mutated oncogene in almost all cancers, including pancreatic ductal carcinoma (PDAC) and colorectal cancer. Increased development efforts targeting KRAS mutations have yielded recent FDA approvals of two KRAS G12C inhibitors. However, this is beneficial for only a small proportion of patients especially with indications like PDAC, where G12C mutations are rare (1%) while G12D mutations account for approximately 45%. Hence, there is a need to develop methods to target a broader range of KRAS mutations. Beyond direct gene mutation, activation of oncogenic KRAS and downstream or parallel signaling through other genomic alterations may benefit from KRAS-specific therapeutic strategies, especially combination therapy. In this study, Optim.AI™, a hybrid computational-experimental platform is harnessed to identify mutation-specific optimal therapies and to stratify efficacious drug combinations containing KRAS inhibitors in pancreatic cancer. METHODS: Optim.AI™ utilizes small datasets to rationally converge upon optimal drug combinations within a defined drug search space. By mapping experimental data points to a second-order quadratic function, Optim.AI™ predicts cell killing efficacies for all possible combinations, independent of the background of the models utilized. In this study, pancreatic cancer cell lines, with varying KRAS mutations, were tested with an array of 155 different combinations consisting of 12 drugs at varying concentrations. The drugs include KRAS G12C inhibitors, Sotorasib and Adagrasib, KRAS inhibitors in development, and approved therapies used clinically for PDAC. Post-drug treatment viability was measured and used for Optim.AI™ analysis, to rank and compare the top therapies across the different cell lines. RESULTS: Through Optim.AI™ analysis, we observed a range of sensitivities towards KRAS inhibition, even for both Sotorasib and Adagrasib. Compared against HT-29 cell line, harboring wild type KRAS, we have identified distinct combinations of KRAS inhibitors with standard of care drugs on cell lines with G12D, G12V and G12C KRAS mutants. Subsequent validation of the top and bottom ranked combinations for the respective cell lines correspond to the predicted Optim.AI™ results. CONCLUSIONS: This study has demonstrated the differential sensitivities of the KRAS inhibitors across several pancreatic cancer cell lines, with different KRAS mutational profiles. Notably, we have identified KRAS inhibitor-based combinations that are distinctly different from wild type KRAS cell line. These preliminary findings could be used to drive precision medicine by identifying suitable biomarkers to potentially stratify pancreatic cancer patients and improve treatment outcomes. Future work includes evaluating the clinical significance of these combinations by validating them on patient-derived organoids. Citation Format: Jhin Jieh Lim, Edward Kai-Hua Chow, Masturah Rashid. KRAS inhibitor-based combinations in pancreatic cancer identified through Optim.AI [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 6492.

Abstract 4560: Assessing cancer drug combination efficacy across 900+ PRISM cell lines in a multiplexed screening assay

Abstract Combination therapies are crucial in cancer care, yet identifying which combination will benefit a specific patient is challenging. The vast array of clinical, investigational, and tool anticancer agents and diverse cancer contexts makes investigating many drug combinations over a large number of cell lines infeasible. Here, we describe a multiplexed screening approach using the PRISM assay, where drug combinations are screened on >900 DNA-barcoded cancer cell lines. We analyze cell viability data in drug combinations and single agents using established metrics to describe the combined effects such as additivity, synergy, and antagonism. Quantifying and classifying combined effects will help prioritize specific combinations for follow-up investigations and provide biological and mechanistic insights, thus enabling rationally designed combination therapies. We illustrate our methodology through data from three drug combinations that exemplify synergy and antagonism. In the first combination, temozolomide+O6-benzylguanine (alkylating agent + MGMT inhibitor), we found that cell lines expressing MGMT were sensitized to temozolomide in the presence of O6-benzylguanine, thus illustrating synergy. In the second combination, we screened two anti-apoptosis compounds A-1331852+AZD5991 (BCL-xL + MCL1 inhibitor) and found that BCL-xL and MCL1 inhibition were also synergistic. In the third combination, ML210+ferrostatin-1 (GPX4 inhibitor inducing ferroptosis + ferroptosis inhibitor), we found that ferrostatin-1 antagonized the effects of ML210. Our analysis also reveals the importance of appropriate dose selection and analytical metrics for reliably measuring combined effects. In conclusion, the PRISM platform's multiplexed cell line screening is a robust method for characterizing rationally designed drug combinations, offering a systematic approach to enhance the precision of combination therapy development in cancer care. Citation Format: Ashish Bino George, Shiker Nair, Mustafa Kocak, Ellen Nguyen, Alvin Kalathungal, Anthony Fazio, Cole Ponsi, Aydin Golabi, Melissa A. Ronan, Matthew G. Rees, Jennifer A. Roth. Assessing cancer drug combination efficacy across 900+ PRISM cell lines in a multiplexed screening assay [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 4560.

Boolean modeling of breast cancer signaling pathways uncovers mechanisms of drug synergy.

Breast cancer is one of the most common types of cancer in females. While drug combinations have shown potential in breast cancer treatments, identifying new effective drug pairs is challenging due to the vast number of possible combinations among available compounds. Efforts have been made to accelerate the process with in silico predictions. Here, we developed a Boolean model of signaling pathways in breast cancer. The model was tailored to represent five breast cancer cell lines by integrating information about cell-line specific mutations, gene expression, and drug treatments. The models reproduced cell-line specific protein activities and drug-response behaviors in agreement with experimental data. Next, we proposed a calculation of protein synergy scores (PSSs), determining the effect of drug combinations on individual proteins' activities. The PSSs of selected proteins were used to investigate the synergistic effects of 150 drug combinations across five cancer cell lines. The comparison of the highest single agent (HSA) synergy scores between experiments and model predictions from the MDA-MB-231 cell line achieved the highest Pearson's correlation coefficient of 0.58 with a great balance among the classification metrics (AUC = 0.74, sensitivity = 0.63, and specificity = 0.64). Finally, we clustered drug pairs into groups based on the selected PSSs to gain further insights into the mechanisms underlying the observed synergistic effects of drug pairs. Clustering analysis allowed us to identify distinct patterns in the protein activities that correspond to five different modes of synergy: 1) synergistic activation of FADD and BID (extrinsic apoptosis pathway), 2) synergistic inhibition of BCL2 (intrinsic apoptosis pathway), 3) synergistic inhibition of MTORC1, 4) synergistic inhibition of ESR1, and 5) synergistic inhibition of CYCLIN D. Our approach offers a mechanistic understanding of the efficacy of drug combinations and provides direction for selecting potential drug pairs worthy of further laboratory investigation.

Quantitative systems pharmacology modeling of HER2-positive metastatic breast cancer for translational efficacy evaluation and combination assessment across therapeutic modalities

HER2-positive (HER2+) metastatic breast cancer (mBC) is highly aggressive and a major threat to human health. Despite the significant improvement in patients’ prognosis given the drug development efforts during the past several decades, many clinical questions still remain to be addressed such as efficacy when combining different therapeutic modalities, best treatment sequences, interindividual variability as well as resistance and potential coping strategies. To better answer these questions, we developed a mechanistic quantitative systems pharmacology model of the pathophysiology of HER2+ mBC that was extensively calibrated and validated against multiscale data to quantitatively predict and characterize the signal transduction and preclinical tumor growth kinetics under different therapeutic interventions. Focusing on the second-line treatment for HER2+ mBC, e.g., antibody-drug conjugates (ADC), small molecule inhibitors/TKI and chemotherapy, the model accurately predicted the efficacy of various drug combinations and dosing regimens at the in vitro and in vivo levels. Sensitivity analyses and subsequent heterogeneous phenotype simulations revealed important insights into the design of new drug combinations to effectively overcome various resistance scenarios in HER2+ mBC treatments. In addition, the model predicted a better efficacy of the new TKI plus ADC combination which can potentially reduce drug dosage and toxicity, while it also shed light on the optimal treatment ordering of ADC versus TKI plus capecitabine regimens, and these findings were validated by new in vivo experiments. Our model is the first that mechanistically integrates multiple key drug modalities in HER2+ mBC research and it can serve as a high-throughput computational platform to guide future model-informed drug development and clinical translation.

Predicting drug synergy using a network propagation inspired machine learning framework.

Combination therapy is a promising strategy for cancers, increasing therapeutic options and reducing drug resistance. Yet, systematic identification of efficacious drug combinations is limited by the combinatorial explosion caused by a large number of possible drug pairs and diseases. At present, machine learning techniques have been widely applied to predict drug combinations, but most studies rely on the response of drug combinations to specific cell lines and are not entirely satisfactory in terms of mechanism interpretability and model scalability. Here, we proposed a novel network propagation-based machine learning framework to predict synergistic drug combinations. Based on the topological information of a comprehensive drug-drug association network, we innovatively introduced an affinity score between drug pairs as one of the features to train machine learning models. We applied network-based strategy to evaluate their therapeutic potential to different cancer types. Finally, we identified 17 specific-, 21 general- and 40 broad-spectrum antitumor drug combinations, in which 69% drug combinations were validated by vitro cellular experiments, 83% drug combinations were validated by literature reports and 100% drug combinations were validated by biological function analyses. By quantifying the network relationships between drug targets and cancer-related driver genes in the human protein-protein interactome, we show the existence of four distinct patterns of drug-drug-disease relationships. We also revealed that 32 biological pathways were correlated with the synergistic mechanism of broad-spectrum antitumor drug combinations. Overall, our model offers a powerful scalable screening framework for cancer treatments.

Emerging treatment strategies in chronic B-cell malignancies—News from ICML 2023

SummaryImplementation of targeted drugs in the treatment landscape of indolent B‑cell malignancies has facilitated long-term remissions and reduced treatment-related toxicities for a substantial proportion of patients across several disease entities. Yet, adverse prognostic genetic aberrations such as TP53 dysfunctions and short-lived remissions after front-line therapy cannot be fully compensated with currently licensed novel agents thereby predisposing affected patients to inferior clinical outcomes in the long run. Moreover, acquired resistance and increasing drug intolerability typically evolving with continuous treatment remain challenges to overcome in daily clinical practice. With these unmet medical needs in mind, several clinical trials are currently investigating the efficacy and feasibility of time-limited novel drug combinations across several indolent B‑cell malignancies. This short review aims to give an update on novel clinical trial data presented at the International Conference of Malignant Lymphoma 2023.

Additivity Predicts the Clinical Efficacy of Approved Combination Therapies.

A drug additivity model accurately matches the clinical efficacy of most approved drug combinations.

Abstract A127: Identification of therapeutic drug combinations targeting KRAS

Abstract KRAS is the most frequently mutated oncogene and increased development efforts targeting KRAS mutations have yielded recent FDA approvals of two KRAS G12C inhibitors. However, for some indications, there are other KRAS mutations more common than G12C. For Pancreatic ductal adenocarcinoma (comprising more than 90% of pancreatic cancer cases), 94% harbor KRAS mutations, where G12D mutations account for around 45% while G12C mutations are rare (1%). Hence, there is a need to develop methods to target a broader range of KRAS mutations. Beyond direct gene mutation, activation of oncogenic KRAS and downstream MAPK signaling through other genomic alterations may benefit from KRAS-specific therapeutic strategies, especially combination therapy. We have developed a platform, Optim.AI™, that rationally converges upon optimal drug combinations within a defined drug search space, without any prior knowledge of the background of the models. In this study, we utilized the platform to identify efficacious drug combinations that contain a histone deacetylase 4/6 inhibitor, KYAN-001 in the context of KRAS mutations. 7 cell lines (6 prostate and pancreatic cancer with normal, epithelial THLE-2 cell line) were evaluated with Optim.AI. The cancer cells and THLE-2 were seeded into 384-well plates and treated with 155 different combinations consisting of indication-specific drugs the following day. Post-drug treatment cell viability was used for Optim.AI™ analysis, which maps experimental data points to a second-order quadratic function to predict and rank cancer cell killing efficacies for all other permutations of drug combinations. In androgen resistant prostate cancer, Optim.AI identified synergistic interaction between KYAN-001 and a MEK inhibitor. With MEK being downstream of the KRAS pathway, we wanted to evaluate the efficacy of this combination on pancreatic cancer. Interestingly, this combination was demonstrated to be top ranked against PANC-1 and AsPC-1 cell lines, both harboring G12D KRAS mutation, as compared to a cell line with wild-type KRAS, BxPC-3. This was further validated via half-maximal inhibitory curves (IC50), where synergism was also observed for the combination on AsPC-1, but not BxPC-3. KYAN-001 was also shown to exhibit monotherapy sensitivity against pancreatic cancer patient-derived organoids (PDOs), with a mean of 4 µM. This study has successfully demonstrated the ability of Optim.AI™ to identify novel, optimal drug combinations for both prostate and pancreatic cancers, without any prior background knowledge on these cell lines. With MEK being downstream of the KRAS pathway and 94% of those diagnosed with pancreatic cancer have KRAS mutations, the differential sensitivities of this combination across the cell lines presents the utility of the platform in precision medicine, especially for undruggable targets like KRAS. With promising and demonstrated monotherapy efficacy of KYAN-001 on PDOs, further experiments could be done to evaluate the clinical relevance of both KYAN-001 and MEK inhibition on other relevant ex vivo models. Citation Format: Masturah Mohd Abdul Rashid, Lisa Chow, Edward Kai-Hua Chow. Identification of therapeutic drug combinations targeting KRAS [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A127.

Valproic acid reprograms the metabolic aberration of cisplatin treatment via ALDH modulation in triple-negative breast cancer cells.

We recently demonstrated that the histone deacetylase inhibitor valproic acid (VPA) reprograms the cisplatin-induced metabolome of triple-negative breast cancer (TNBC) cells, including a shift in hexose levels. Accordingly, here, we tested the hypothesis that VPA alters glucose metabolism in correlation with cisplatin sensitivity. Two TNBC cell lines, MDA-MB-231 (a cisplatin-resistant line) and MDA-MB-436 (a cisplatin-sensitive line), were analyzed. The glycolysis and oxidative metabolism were measured using the Glycolysis Stress Test kit. The expression of aldehyde dehydrogenases (ALDHs), enzymes linked to drug resistance, was investigated by Western blot and real-time PCR analyses. We additionally studied the influence of ALDH inhibition by disulfiram on the viability of MDA-MB-231 cells and on a TNBC patient-derived organoid system. Cisplatin treatment reduced the extracellular acidification rate in MDA-MB-436 cells but not MDA-MB-231 cells, whereas VPA addition increased the extracellular acidification rate in both cell lines. VPA further reduced the oxygen consumption rate of cisplatin-treated MDA-MB-436 cells, which correlated with cell cycle alterations. However, in MDA-MB-231 cells, the cell cycle distribution did not change between cisplatin/VPA-cisplatin treatments. In both cell lines, VPA increased the expression of ALDH isoform and ALDH1A1 expression. However, only in MDA-MB-231 cells, VPA synergized with cisplatin to augment this effect. Disulfiram sensitized the cells to the cytotoxic effects of the VPA-cisplatin combination. Furthermore, the disulfiram-VPA-chemotherapy combination was most effective in TNBC organoids. Our results show that ALDH overexpression may act as one mechanism of cellular resistance to VPA in TNBC and that its inhibition may enhance the therapeutic efficacy of VPA-chemotherapeutic drug combinations.

Optimizing the Combination of Cytotoxic Drugs Along with Radiotherapy as Effective Treatment for Extranodal NK/T-Cell Lymphoma.

The optimal combination of cytotoxic drugs along with radiotherapy (RT) is unknown. We undertook multidrug screening process to identify the most efficacious cytotoxic drugs, and appraise the efficacy of various drug combinations. We reviewed 3105 patients who received 40 chemotherapy regimens with different combinations of nine drug classes and/or RT. Least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression analyses were used to screen efficacious single drugs and identify optimal combinations for overall survival (OS). Inverse probability of treatment weighting (IPTW) and multivariable analyses were used to compare survival between treatment regimens. Screening and validation revealed RT, asparaginase (ASP), and gemcitabine (GEM) to be the most efficacious single modality/drugs. RT remained an important component of first-line treatment, whereas ASP was a fundamental drug of non-anthracycline (ANT)-based regimens. Addition of RT to non-ANT-based or ASP/GEM-based regimens, or addition of an ASP-drug into ANT-based or GEM/PLA-based regimens, improved 5-year OS significantly. Use of ASP/GEM-based regimens led to significantly higher 5-year OS (79.9%) compared with ASP/ANT-based (69.2%, P = 0.001), ASP/MTX-based (63.5%, P = 0.011), or ASP/NOS-based (63.2%, P<0.001) regimens. The survival benefit of ASP/GEM-based regimens over other ASP-based regimens was substantial across risk-stratified and advanced-stage subgroups. The survival benefits of a combination of RT, ASP, and GEM were consistent after adjustment for confounding factors by IPTW. These results suggest that combining ASP/GEM with RT for ENKTCL is an efficacious and feasible therapeutic option, and provides a rationale and strategy for developing combination therapies.