Effective Drug Combinations Research Articles

Uterine infusion strategies for infertile patients with recurrent implantation failure: a systematic review and network meta-analysis

BackgroundIntra-uterine infusion treatments were reported to be beneficial to embryo implantation and pregnancy outcomes, and considered as potential therapies for infertile patients with recurrent implantation failure (RIF). Nevertheless, their efficiencies were controversial and there lack of consensus on which intrauterine treatment is the most effective.MethodsAll prospective trials (in Chinese or English) were searched in Databases PubMed, Cochrane, Web of Science, and CNKI from July 2013 to July 2023. We included studies that investigated various uterine infusions, including chorionic gonadotropin, granulocyte colony-stimulating factor, monocytes, platelet-rich plasma, etc. during IVF treatment and reported subsequent pregnancy outcomes.ResultsWe finally included 56 researches, including 40 randomized controlled trials, 14 non-randomized controlled trials, and 3 prospective cohort studies. This study included a total of 11 uterine perfusion methods: Placebo, Human Chorionic Gonadotropin (HCG), Granulocyte Colony-Stimulating Factor (G-CSF), platelet-rich plasma (PRP), Peripheral Blood Mononuclear Cell (PBMC), Growth hormone (GH), dexamethasone (DEX), Embryo culture supernatant (ESC), PRP combined with G-CSF (PRP + G-CSF), RPR combined with subcutaneous injection of G-CSF (RPR + G-CSFsc), G-CSF combined with subcutaneous injection of AXaIU (G-CSF + AXaIUsc). Intrauterine infusion of HCG, PBMC, G-CSF, and PRP significantly improves pregnancy outcomes in patients with repeated implantation failure compared with blank controls or placebo, and PRP improved the clinical pregnancy and live birth most. GH and ESC infusion might improve the pregnancy outcomes, but uterine infusion of DEX was shown with high miscarriage. The combination therapy did not show a significant advantage over the mono-therapy.ConclusionsIntrauterine infusion of HCG, PBMC, G-CSF, and PRP are promising strategies for improving pregnancy outcomes for infertile patients with recurrent implantation failure. Among these treatments, PRP may be the best. More researches are required to explore the effect of drug combinations and less commonly used drugs as well.Trial registrationOur study was registered in PROSPERO and the ID was CRD42023467188.

A Real-world Toxicity Atlas Shows that Adverse Events of Combination Therapies Commonly Result in Additive Interactions.

Combination therapies are a promising approach for improving cancer treatment, but it is challenging to predict their resulting adverse events in a real-world setting. We provide here a proof-of-concept study using 15 million patient records from the FDA Adverse Event Reporting System (FAERS). Complex adverse event frequencies of drugs or their combinations were visualized as heat maps onto a two-dimensional grid. Adverse event frequencies were shown as colors to assess the ratio between individual and combined drug effects. To capture these patterns, we trained a convolutional neural network (CNN) autoencoder using 7,300 single-drug heat maps. In addition, statistical synergy analyses were performed on the basis of BLISS independence or χ2 testing. The trained CNN model was able to decode patterns, showing that adverse events occur in global rather than isolated and unique patterns. Patterns were not likely to be attributed to disease symptoms given their relatively limited contribution to drug-associated adverse events. Pattern recognition was validated using trial data from ClinicalTrials.gov and drug combination data. We examined the adverse event interactions of 140 drug combinations known to be avoided in the clinic and found that near all of them showed additive rather than synergistic interactions, also when assessed statistically. Our study provides a framework for analyzing adverse events and suggests that adverse drug interactions commonly result in additive effects with a high level of overlap of adverse event patterns. These real-world insights may advance the implementation of new combination therapies in clinical practice.

Revisiting the Checkerboard to Inform Development of β-Lactam/β-Lactamase Inhibitor Combinations.

A two-dimensional "checkerboard" array employing systematic titration (e.g., serial two-fold dilutions) is a well-established in vitro method for exploring the antibacterial effects of novel drug combinations. Minimum inhibitory concentrations (MICs) on the checkerboard are isoeffective points at which the antibiotic potency is the same. Representations of checkerboard MIC curves for a β-lactam and β-lactamase inhibitor combination are used in hypothetical "thought experiments" and reveal the ways in which current practices can be improved. Because different types of response (i.e., independence vs. additivity vs. one effective agent; interaction vs. noninteraction) produce different MIC curves, data from different strains/isolates should not be pooled indiscriminately, as the composition of a pooled dataset will influence any derived pharmacokinetic/pharmacodynamic (PK/PD) index. Because the β-lactamase inhibitor threshold concentration (CT) parameter is a function of the β-lactam partner dosing regimen, it is not possible to derive a universal PK/PD index target based on CT. Alternative susceptibility testing methods represent different planes through the checkerboard; a fixed ratio method is less prone to bias for all β-lactam and β-lactamase inhibitor combinations. Susceptibility test MICs will often not reflect the sensitivity of the strain/isolate to the β-lactamase inhibitor, so the use of these MICs to normalize PK/PD indices is inappropriate.

Evaluation of the in vitro susceptibility of clinical isolates of NDM-producing Klebsiella pneumoniae to new antibiotics included in a treatment regimen for infections.

Due to the growing resistance to routinely used antibiotics, the search for new antibiotics or their combinations with effective inhibitors against multidrug-resistant microorganisms is ongoing. In our study, we assessed the in vitro drug susceptibility of Klebsiella pneumoniae strains producing New Delhi metallo-β-lactamases (NDM) to antibiotics included in the Infectious Diseases Society of America (IDSA) and European Society of Clinical Microbiology and Infectious Diseases (ESCMID) recommendations. A total of 60 strains of NDM-producing K. pneumoniae were obtained from different patients hospitalized at the 4th Military Hospital in Wroclaw between 2019 and 2022 and subjected to drug susceptibility to selected antibiotics, including the effects of drug combinations. Among the tested antibiotics, the highest sensitivity (100%) was observed for cefiderocol, eravacycline (interpreted according to the European Committee on Antimicrobial Susceptibility Testing [EUCAST]), and tigecycline. Sensitivity to intravenous fosfomycin varied depending on the method used. Using the "strip stacking" method, determining cumulative sensitivity to ceftazidime/avibactam and aztreonam demonstrated 100% in vitro sensitivity to this combination among the tested strains. The in vitro susceptibility assessment demonstrated that, the best therapeutic option for treating infections caused by carbapenemase-producing strains seems to be a combination of ceftazidime/avibactam with aztreonam. Due to the safety of using both drugs, cost effectiveness, and the broadest indications for use among the tested antibiotics, this therapy should be the first-line treatment for carbapenemase-producing Enterobacterales infections. Nevertheless, a comprehensive evaluation of the efficacy of treating infections caused by NDM-producing K. pneumoniae strains should include not only in vitro susceptibility assessment but also an analysis of clinical cases.

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.

Sacubitril/valsartan reversal of left ventricular remodeling is associated with improved hemodynamics in resistant hypertension

BackgroundSacubitril/valsartan (S/V) has been shown to be an effective antihypertensive drug combination. However, its therapeutic effects on blood pressure (BP), hemodynamics, and left ventricular (LV) remodeling in resistant hypertension (RHTN) remain unclear. MethodsEighty-six patients completed this self-control study, during which olmesartan was administered within the first 8 weeks (phase 1), followed by S/V within the second 8 weeks (phase 2), with nifedipine and hydrochlorothiazide taken as background medications. Office BP, echocardiography, and hemodynamics assessment using impedance cardiography were performed at baseline and at the eighth and sixteenth weeks. ResultsThe reduction in office BP was larger in phase 2 than in phase 1 (19.59/11.66 mmHg vs. 2.88/1.15 mmHg). Furthermore, the treatment in phase 2 provided greater reductions in systemic vascular resistance index (SVRI) and thoracic blood saturation ratio (TBR), with differences between the two phases of −226.59 (−1212.80 to 509.55) dyn·s/cm5/m2 and −0.02 (−0.04 to 0.02). Switching from olmesartan to S/V also significantly reduced E/E′, LV mass index, LV end-diastolic volume index, and LV end-systolic volume index (all P < 0.05). Decreases in arterial stiffness, SVRI, and TBR were correlated with changes in indicators of LV remodeling (all P < 0.05). This correlation persisted even after adjusting for confounders including changes in BP. ConclusionsSwitching from olmesartan to S/V effectively lowered BP and reversed ventricular remodeling in RHTN. In addition, hemodynamic improvement was also observed. Changes in hemodynamics played an important role in reversing LV remodeling of S/V, and were independent of its antihypertensive effect.

Abstract 947: A nanodroplet cell processing platform for identifying synergistic drug combinations for neuroblastoma treatment in TH-MYCN transgenic mice

Abstract Neuroblastoma is the most common pediatric extracranial solid tumor and contributes to more than 15% of child mortality. It is characterized by extreme heterogeneity and high-risk tumors often resulting in poor prognosis. Currently, chemotherapy is a prevailing standard treatment for neuroblastoma. However, it is plagued by multiple challenges such as drug resistance and debilitating side effects. Synergistic drug combination is proposed as an innovative approach to potentially improve clinical outcomes of single-agent chemotherapy. We propose a novel approach to screen for effective chemo-synergistic drug combinations using Bioinspired nanodroplet processing (BioNDP) screening platform. This platform overcomes the limitations of conventional drug screening approaches, including the requirement for large cell numbers and the low translation rate from bench to bedside. In this study, we utilize TH-MYCN transgenic mice, a well-known genetically engineered mouse model of neuroblastoma that recapitulates critical genetic and clinical aspects of high-risk MYCN-amplified neuroblastoma. With its genetically homogeneous background and spontaneous tumor development, TH-MYCN mice offer a valuable comparison to traditional xenograft mouse models. We have also developed an optimal procedure of primary murine tumor isolation using both negative and positive selection approaches. The purity of the isolated cells was verified with GD2 expression, one of well-known marker of neuroblastoma. In addition, we have successfully identified an effective drug combination of cyclophosphamide, doxorubicin, and vincristine, using primary neuroblastoma tumor samples derived from TH-MYCN transgenic mice as a target. The in vitro synergistic effect of primary mouse tumor cell cytotoxicity was confirmed by using luminescent cell viability assay. Further in vivo experiments have demonstrated the therapeutic efficacy of this selected drug combination in suppressing tumor growth and improving survival rate in all TH-MYCN transgenic mice treated. To sum up, we aim to establish a future patient-centric paradigm by which minimal primary cell samples from a personalized biopsy of patients may undergo drug screening, leading to the identification of a tailored drug combination. The BioNDP platform we developed may become a pivotal resource for personalized therapy to ensure a faster and more efficient treatment strategy for patients. Citation Format: Yen-Tzu Liao, Zhi-Kai Yu, Ching-Te Kuo, Wen-Ming Hsu, Yi-Xun Huang, Hsinyu Lee. A nanodroplet cell processing platform for identifying synergistic drug combinations for neuroblastoma treatment in TH-MYCN transgenic mice [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 947.

Abstract 4936: An ultrahigh-throughput synergy screening platform enables discovery of novel drug combinations

Abstract The battle against many cancers and infectious diseases has long been hindered due to the complexity of finding potent and effective drug combinations. With each new drug considered, the number of combinations to potentially test increases exponentially, posing substantial challenges in screening throughput. These challenges further intensify when accounting for the number of doses of each drug that need to be tested in a drug combination matrix (known as matrix density). There is a pressing need to screen large amounts of combinations at sufficient density to discover new therapies for diseases like cancer, but this has traditionally been out of reach. However, the recent widespread adoption of acoustic liquid handling robots has shown promise to overcome these obstacles by allowing for intricate drug screening template designs which were previously not possible to make. Despite these advances, the throughput achieved by these technologies has been limited due to lack of broadly accessible protocols and analytical tools for drug combination screening. We present Combocat, an end-to-end platform that allows for substantial increases in throughput of drug combination screens by combining experimental protocols that can be deployed for acoustic liquid handlers, machine learning algorithms for data imputation, and software that allows for in-depth analysis of results. We first generated a reference dataset of over 250,000 unique drug combination measurements in multiple cancer cell lines. The combination data were collected in a dense format (10×10 combination matrices) using a novel drug-drug template and achieved a dramatic increase in throughput compared to conventional methods. We then used this dataset to build a computational model which allowed us to accurately estimate drug combination effects using sparse measurements and imputing non-measured values with machine learning. The sparse measurements are collected in 1536-well microplates and substantially boost the throughput capabilities of drug-drug screens. As proof-of-concept, we used our method to screen a preclinical model of neuroblastoma with 9,045 drug combinations. This represents 10% the scale of the largest drug combination studies ever reported, achieved using a fraction of the resources, and in dense formats. We validated our findings by re-screening top hits using the fully-measured, non-imputed method and demonstrate the accuracy of our platform. The Combocat platform’s documentation and codebase is open-source, and we also make a GUI available for interactive exploration of screening results. By integrating advanced experimental and computational methods, we provide a generalizable pipeline that will expedite synergy screens and the drug combination discovery process for many diseases. Citation Format: William C. Wright, Paul Geeleher. An ultrahigh-throughput synergy screening platform enables discovery of novel drug combinations [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 4936.

Abstract 7169: A real-world toxicity-atlas shows that adverse events of combination-therapies commonly result in additive interactions

Abstract Purpose: Combination-therapies are a promising approach for improving cancer treatment, but it is challenging to predict their resulting adverse events in an real-world setting. Experimental Design: We provide here a proof-of-concept study using 15 million patient records from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Complex adverse-event frequencies of drugs or their combinations were visualized as heatmaps onto a 2D UMAP grid. Adverse event frequencies were shown as colors to assess the ratio between individual and combined drug effects. To capture these patterns, we trained a convolutional neural network (CNN) autoencoder using 7,300 single-drug heatmaps. Additionally, statistical synergy analyses were performed based on BLISS independence or Chi-square testing. Results: The trained CNN model was able to decode patterns, showing that adverse events occur in global rather than isolated and unique patterns. Patterns were not likely to be attributed to disease symptoms given their relatively limited contribution to drug associated adverse events. Pattern recognition was validated using trial data from ClinicalTrials.gov and drug combination data. We examined the adverse-event interactions of 140 drug combinations known to be avoided in the clinic and found that near all of them showed additive rather than synergistic interactions, also when assessed statistically. Conclusion: Our study provides a framework for analyzing adverse events and suggest that adverse drug interactions commonly result in additive effects with a high level of overlap of adverse event patterns. These real-world insights may advance the implementation of new combination therapies in clinical practice. Citation Format: Asli Küçükosmanoglu, Silvia I. Scoarta, Bart Westerman. A real-world toxicity-atlas shows that adverse events of combination-therapies commonly result in additive interactions [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 7169.

Abstract 3237: KDM1A/LSD1 inhibition enhances chemotherapy response in ovarian cancer

Abstract Background: Ovarian cancer (OCa) is the deadliest of all gynecological cancers. The standard treatment for OCa is platinum-based chemotherapy, such as carboplatin or cisplatin in combination with paclitaxel. Most patients are initially responsive to these treatments; however, nearly 90% will develop recurrence and inevitably succumb to chemotherapy-resistant disease. Recent studies have revealed that the epigenetic modifier lysine-specific histone demethylase (KDM1A/LSD1) is highly overexpressed in OCa. However, whether KDM1A plays a role in chemoresistance and whether its inhibition enhances chemotherapy response in OCa is unknown. In this study, we explored the hypothesis that KDM1A inhibition will sensitize OCa cells to standard-of-care chemotherapy. Methods: Transduction of KDM1A-specific shRNA was used to generate KDM1A knockdown (KD) cells. Cell viability, colony formation, and apoptosis assays were used to determine the impact of KDM1A KD on sensitization of SKOV3, OVCAR8, and OVCAR5 OCa cells to cisplatin, carboplatin, and paclitaxel. The combination effect of KDM1A inhibitors (NCD38 and SP2509) and chemotherapy drugs was further determined. Mechanistic studies were conducted using RNA-sequencing, RT-qPCR, and Western blotting. In vivo efficacy ofKDM1A knockdown with or without chemotherapy was determined using OCa orthotopic xenograft models. Results: Analysis of TCGA datasets revealed that KDM1A expression is negatively correlated with OCa survival, and its expression is high in patients who do not respond to chemotherapy. Further, Western blotting results show that treatment with chemotherapy drugs cisplatin, carboplatin, and paclitaxel increased KDM1A expression in OCa cells. KDM1A-KD or KDM1A inhibition sensitized OCa cells to chemotherapy drugs in reducing cell viability, survival, and enhancing apoptosis. Moreover, knockdown of KDM1A sensitized carboplatin-resistantA2780-CP70 cells to carboplatin treatment. RNA-seq studies revealed that KDM1A-KD and cisplatin-regulated genes showed negative correction with epithelial-mesenchymal transition (EMT) and positive correlation with apoptosis-related pathways. Importantly, KDM1A KD in combination with cisplatin significantly reduced the tumor growth in the orthotopic OCa xenograft model. Conclusion: These results suggest that the combination of KDM1A inhibitors with chemotherapy is a viable therapeutic approach for the treatment of OCa Citation Format: Jessica D. Johnson, Yihong Chen, Sridharan Jayamohan, Yi He, Diksha Jamwal, Neetesh Pandey, Edward Kost, Ratna K. Vadlamudi, Gangadhara R. Sareddy. KDM1A/LSD1 inhibition enhances chemotherapy response in ovarian 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 3237.

Abstract 6446: ResponderID™ KRAS: Biology-driven machine learning to personalize KRAS inhibitor therapeutics

Abstract Breakthroughs in targeted KRAS therapeutics (KRASi) have the potential to transform the treatment landscape for several of the most common cancers including lung, colorectal, and pancreatic. Despite the recent approvals of KRASi and the anticipation of more to come, both the rate of patient response and the durability of these responses remain significant areas requiring improvement. Biomarkers that can predict response to KRASi and guide effective patient selection and drug combination strategies will be key to realizing the full potential of this emerging therapeutic field. While most biomarkers predominantly rely on a single analyte (e.g. KRAS mutation status), Genialis’ biomarkers are constructed using high-dimensional and/or multimodal data that capture the underlying biological complexity unique to each individual patient. Genialis' ResponderID™ is a machine learning-based biomarker discovery framework that models fundamental aspects of cancer biology to predict the clinical benefit based on the patient’s own biology. Here we report progress towards the development of a first-in-class, RNA-based biomarker, ResponderID™ KRAS, capable of stratifying KRAS G12C inhibitor response in lung cancer patients using RNA sequencing data. Trained on thousands of lung cancer samples, our biomarker models therapeutic response by unifying two core KRAS biologic axes, dependency and activation, to identify those patients most likely to respond. The performance characteristics of ResponderID™ KRAS thus far has been evaluated on a real world dataset of lung cancer patients treated with Sotorasib. ResponderID™ KRAS serves as an independent biomarker designed to inform clinical trial design, select for therapeutic efficacy, identify rational combination strategies, and expedite approvals across various therapeutic contexts. Citation Format: Josh Wheeler, Anže Lovše, Klemen Žiberna, Miha Štajdohar, Luka Ausec, Janez Kokošar, Daniel Pointing, Aditya Pai, Rafael Rosengarten, Mark Uhlik. ResponderID™ KRAS: Biology-driven machine learning to personalize KRAS inhibitor therapeutics [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 6446.

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.

Abstract 891: Turning data into information: Using PD3D® models to guide colorectal cancer therapy by Optim.AITM

Abstract Colorectal cancer (CRC) is one of the most prevalent and lethal malignancies globally with up to 50 % of patients eventually progressing to metastatic disease. The mitogen-activated-protein kinase (MAPK) pathway emerges as a key player, being one of the most frequently mutated signaling pathways in the oncogenesis of CRC. However, given the numerous genomic aberrations and tumor heterogeneity in CRC, patients may benefit from combinatorial therapies, particularly those who have inoperable or metastatic tumors. In the present study, we investigated the feasibility of combining two platforms, patient-derived 3D (PD3D®) models with Optim.AI™, to identify more effective cancer therapies. PD3D® models can robustly retain the genotypic and histopathologic features of the primary patient tumor, model tumor heterogeneity and were shown to predict a patient’s drug response. Optim.AI™ is a hybrid computational-experimental platform that uses small data sets to rationally converge to optimal drug combinations within a defined drug search space. By mapping experimental data points to a second-order quadratic function, Optim.AI™ can predict every possible 531k data points and thus the cell-killing efficacy for all other possible combinations without testing each individual drug-dose combination. Two CRC PD3D® models with different mutation profiles were tested with 155 different combinations of 12 drugs at variant concentrations. The post-treatment cell viability was measured and used for Optim.AI™ analysis to evaluate and compare the best therapies. Optim.AI™ analysis revealed differential drug sensitivity between tested CRC PD3Ds®. The top-ranked drug combinations included SN-38, active compound of commonly used chemotherapeutic irinotecan, paired with MEK-inhibitors trametinib or cobimetinib which we could confirm with both platforms. With this study, we successfully demonstrated the feasibility, the robustness, and the efficiency of combining PD3Ds® and Optim.AI™ in identifying effective drug combination therapies, here for CRC, within one month. This combined technology provides precise insights into tumor treatability and its functional causes of treatment outcomes, leading to new treatment combinations and accelerating the development of new cancer drugs in a time- and cost-effective manner. Citation Format: Ulrike Pfohl, Masturah Mohd Abdul Rashid, Jhin Jieh Lim, Juergen Loskutov, Lena Wedeken, Edward Kai-Hua Chow, Hugo Saavedra, Christoph Reinhard, Christian R. Regenbrecht. Turning data into information: Using PD3D® models to guide colorectal cancer therapy by Optim.AITM [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 891.

Abstract 3341: Targeting CXCR4 via the small molecule inhibitor NMX1 as a therapeutic strategy to treat high-risk malignancies

Abstract Introduction: Cytokines are secreted by various cells of the immune system, endothelium, and stroma that play a critical role in cell survival. The activation of C-X-C chemokine receptor type 4 (CXCR4) by its ligand SDF-1 leads to signaling by the AKT and mTOR pathways. CXCR4 is highly expressed in several types of cancer and contributes to tumor growth, metastasis, and resistance to therapy. Interleukin 11 (IL-11) is a cytokine that often drives disease progression, as well as contributing to tumor immune evasion. NMX1 (NovoMedix) is a novel oral small molecule that inhibits mTOR signaling, CXCR4 expression, IL-11 secretion, and activates AMPK. In animal models of breast cancer, NMX1 effectively prevented tumor growth and metastasis, while also protecting from doxorubicin-induced cardiotoxicity. In this study, we investigated the anticancer activity of NMX1 in preclinical models of high-risk pediatric and adult malignancies. Methods: Cell viability assays using alamar blue were performed in a panel of pediatric and adult cancer cells, including leukemia, brain, breast, lung, neuroblastoma, and sarcoma. Cells were treated with increasing concentrations of NMX1 ranging from 250 nM to 16 µM for 96 hours, followed by determination of half maximal inhibitory concentrations (IC50). To validate target modulation, the level of IL-11 and CXCR4 was determined by ELISA and immunoblot, respectively. Protein kinase activity was assessed by phosphorylation levels. Induction of apoptosis was confirmed by caspase-mediated PARP cleavage. To identify effective drug combinations, NMX1-treated cells were screened with a comprehensive drug library. Synergy was evaluated in the top candidates by dose-response matrices and SynergyFinder. Safety and tolerability was tested in animal models. Results: NMX1 treatment induced cytotoxicity at micromolar concentrations in diverse types of cancer, with IC50 values ranging from 0.2-8.7 µM and 0.4-4.4 µM for leukemia and solid tumor cells, respectively. NMX1 induced cancer cell death by PARP cleavage and apoptosis. Mechanistically, CXCR4 expression, mTOR signaling, and IL-11 secretion were decreased upon treatment with NMX1, validating effective target modulation. The drug combination screen identified several candidates in a tumor type-dependent manner. Synergistic effects were observed using NMX1 combined with clinically relevant concentrations of rucaparib, linsitinib, and crizotinib, which target PARP, IGFR, and c-MET, respectively. Conclusions: Our study shows antitumor activity by NMX1 through inhibition of CXCR4, IL-11, and mTOR signaling, providing an effective therapeutic strategy for high-risk and refractory malignancies. Preclinical data on feasible drug combinations and biological correlates of activity support development of early phase clinical studies involving NMX1. Citation Format: Patrick Sipila, Son Tran, Chunfen Zhang, Laura G. Corral, Kyle W. Chan, Cathy A. Swindlehurst, Aru Narendran. Targeting CXCR4 via the small molecule inhibitor NMX1 as a therapeutic strategy to treat high-risk malignancies [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 3341.

Abstract 6890: 2D Tumor cell culture based Ex vivo drug sensitivity screening platform for South African patient samples (leukemia, multiple myeloma, ovarian and uterine cancer) demonstrate potential for targeted cancer therapies

Abstract Resistance to standard chemotherapy is a major challenge with Leukemia (Leuk), Multiple Myeloma (MM), Ovarian Cancer (OvCa) and Uterine Cancer (UtCa) treatments, which ultimately leads to relapse. Our study objective is to establish a robust drug sensitivity screening platform that can identify drugs and drug combinations that are effective in precision medicine for individual South African patient samples (Leuk, MM, OvCa and UtCa). Given that resistance to chemotherapy treatment accounts for high mortality and morbidity, we focus on the hypothesis that several drug combinations will be required for patients to achieve better response for drug regimens beyond first line of chemotherapy. Therefore, our defined objectives were to 1) establish 2D ex vivo tumour cell culturing using South African patient samples; 2) develop a drug sensitivity screening platform through which selected single and drug combinations will be identified; 3) to provide South African cancer (OvCa, Ut Ca, CLL, MM) patients with individualized treatment options with effective drug combinations for targeted therapies. Through collaboration with, Steve Biko Hospital Pretoria, Chris Hani Baragwanath Hospital and Wits Donald Gordon Medical Centre, Johannesburg, South Africa, we performed 100 patient sample collections including Acute myeloid leukaemia (AML) (n=7), Chronic lymphocytic leukaemia (CLL) (n=4), Chronic myeloid leukaemia (CML) (n= 30), MM (n=40), OvCa (n=10), UtCa (n=4) and healthy donors (n=5). Culture setting for all patient-derived tumor cells were established through the 2D cancer cell culturing in 96 well plates. This was followed by functional drug sensitivity testing using a panel of FDA approved chemotherapy drugs. The average turnaround time from biopsy to drug sensitivity test results is currently at 14 days. We have completed ex vivo drug sensitivity screening to perform analysis on patient samples (n=80) using 30 drugs with a concentration range of 1-1000 nm. Our preliminary drug sensitivity screening studies show good response to proteosome inhibitors, melphalan, doxorubicin and kinase inhibitors. We have established a wound healing assay for OvCa and UtCa samples and demonstrated sustained cell growth for the period of 2 weeks with tumor cells growing faster than twin normal tissue. We are now at stage of performing drug sensitivity screening for OvCa and UtCa patient samples for both twin normal and tumor tissues. We have established a drug sensitivity screening platform and wound healing assay with primary patient derived cells that utilizes South African Patient samples to select effective drugs for monotherapy and also drug combinations. Citation Format: Mutsa Takundwa, Vanelle Kenmogne, Thudzelani Malise, Bernice Monchusi, Phumuzile Dube, Ekene Nweke, June Fabian, Heather Maher, Justin du Toit, Vinitha Philip-Cherian, Pascaline Fru, Adekunle Sajo, Arnold (Arrie) Mouton, Matthys C. van Aardt, Cathy Visser, Greta Dreyer, Deepak Govindaraj. 2D Tumor cell culture based Ex vivo drug sensitivity screening platform for South African patient samples (leukemia, multiple myeloma, ovarian and uterine cancer) demonstrate potential for targeted cancer therapies [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 6890.

Early elevated IFNα is a key mediator of HIV pathogenesis

BackgroundA complete understanding of the different steps of HIV replication and an effective drug combination have led to modern antiretroviral regimens that block HIV replication for decades, but these therapies are not curative and must be taken for life. “Elite controllers” (ECs) is a term for the 0.5% of HIV-infected persons requiring no antiretroviral therapy, whose status may point the way toward a functional HIV cure. Defining the mechanisms of this control may be key to understanding how to replicate this functional cure in others.MethodsIn ECs and untreated non-EC patients, we compared IFNα serum concentration, distribution of immune cell subsets, and frequency of cell markers associated with immune dysfunction. We also investigated the effect of an elevated dose of IFNα on distinct subsets within dendritic cells, natural killer cells, and CD4+ and CD8 + T cells.ResultsSerum IFNα was undetectable in ECs, but all immune cell subsets from untreated non-EC patients were structurally and functionally impaired. We also show that the altered phenotype and function of these cell subsets in non-EC patients can be recapitulated when cells are stimulated in vitro with high-dose IFNα.ConclusionsElevated IFNα is a key mediator of HIV pathogenesis.

Curcumin/TGF-β1 siRNA loaded solid lipid nanoparticles alleviate cerebral injury after intracerebral hemorrhage by transnasal brain targeting

Intracerebral hemorrhage (ICH) is a prevalent cerebrovascular disorder. The inflammation induced by cerebral hemorrhage plays a crucial role in the secondary injury of ICH and often accompanied by a poor prognosis, leading to disease exacerbation. However, blood-brain barrier (BBB) limiting the penetration of therapeutic drugs to the brain. In this paper, our primary objective is to develop an innovative, non-invasive, safe, and targeted formulation. This novel approach aims to synergistically harness the combined therapeutic effects of drugs to intervene in inflammation via a non-injectable route, thereby significantly mitigating the secondary damage precipitated by inflammation following ICH. Thus, a novel “anti-inflammatory” cationic solid lipid nanoparticles (SLN) with targeting ability were constructed, which can enhance the stability of curcumin(CUR) and siRNA. We successfully developed SLN loaded with TGF-β1 siRNA and CUR (siRNA/CUR@SLN) that adhere to the requirements of drug delivery system by transnasal brain targeting. Through the characterization of nanoparticle properties, cytotoxicity assessment, in vitro pharmacological evaluation, and brain-targeting evaluation after nasal administration, siRNA/CUR@SLN exhibited a nearly spherical structure with a particle size of 125.0±1.93 nm, low cytotoxicity, high drug loading capacity, good sustained release function and good stability. In vitro anti-inflammatory results showcasing its remarkable anti-inflammatory activity. Moreover, in vivo pharmacological studies revealed that siRNA/CUR@SLN can be successfully delivered to brain tissue. Furthermore, it also elicited an effective anti-inflammatory response, alleviating brain inflammation. These results indicated that favorable brain-targeting ability and anti-inflammatory effects of siRNA/CUR@SLN in ICH model mice. In conclusion, our designed siRNA/CUR@SLN showed good brain targeting and anti-inflammatory effect ability after nasal administration, which lays the foundation for the treatment of inflammation caused by ICH and offers a novel approach for brain-targeted drug delivery and brings new hope.

Effectiveness of Vasopressin Against Cardiac Arrest: A Systematic Review of Systematic Reviews.

This systematic review (SR) of SRs evaluates the effectiveness of vasopressin alone or in combination with other drugs in improving the outcomes of cardiac arrest (CA). Using a three-step approach, we searched five databases to identify all relevant SRs. Two reviewers independently selected suitable studies, assessed study quality, and extracted relevant data. If an outcome was reported by multiple SRs, a re-meta-analysis was conducted as needed; otherwise, a narrative analysis was performed. Twelve SRs covering 16 original studies were included in this review. The meta-analysis results revealed a significant increase in survival to hospital admission for patients with in-hospital CA (IHCA) or out-of-hospital CA (OHCA) receiving vasopressin alone compared with that for those receiving epinephrine alone. Furthermore, the return of spontaneous circulation (ROSC) was significantly increased in patients with OHCA receiving vasopressin with epinephrine compared with that in those receiving epinephrine alone. Compared with patients with IHCA receiving epinephrine with placebo, those receiving vasopressin, steroids, and epinephrine (VSE) exhibited significant increases in ROSC, survival to hospital discharge, favorable neurological outcomes, mean arterial pressure, renal failure-free days, coagulation failure-free days, and insulin requirement. VSE is the most effective drug combination for improving the short- and long-term outcomes of IHCA. It is recommended to use VSE in patients with IHCA. Future studies should investigate the effectiveness of VSE against OHCA and CA of various etiologies, the types and standard dosages of steroids for cardiac resuscitation, and the effectiveness of vasopressin-steroid in improving CA outcomes.

Large-scale Pan-cancer Cell Line Screening Identifies Actionable and Effective Drug Combinations.

We present the largest cancer drug combination screen published to date with 7 × 7 concentration response matrices for 109 combinations in more than 750 cell lines, complemented by multi-omics predictors of response and identification of "emergent" combination biomarkers. We prioritize hits to optimize clinical translatability, and experimentally validate novel combination hypotheses. This article is featured in Selected Articles from This Issue, p. 695.

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.