Optimal Drug Combinations Research Articles

MIRD Pamphlet No. 31: MIRDcell V4-Artificial Intelligence Tools to Formulate Optimized Radiopharmaceutical Cocktails for Therapy.

Radiopharmaceutical cocktails have been developed over the years to treat cancer. Cocktails of agents are attractive because 1 radiopharmaceutical is unlikely to have the desired therapeutic effect because of nonuniform uptake by the targeted cells. Therefore, multiple radiopharmaceuticals targeting different receptors on a cell is warranted. However, past implementations invivo have not met with convincing results because of the absence of optimization strategies. Here we present artificial intelligence (AI) tools housed in a new version of our software platform, MIRDcell V4, that optimize a cocktail of radiopharmaceuticals by minimizing the total disintegrations needed to achieve a given surviving fraction (SF) of tumor cells. Methods: AI tools are developed within MIRDcell V4 using an optimizer based on the sequential least-squares programming algorithm. The algorithm determines the molar activities for each drug in the cocktail that minimize the total disintegrations required to achieve a specified SF. Tools are provided for populations of cells that do not cross-irradiate (e.g., circulating or disseminated tumor cells) and for multicellular clusters (e.g., micrometastases). The tools were tested using model data, flow cytometry data for suspensions of single cells labeled with fluorochrome-labeled antibodies, and 3-dimensional spatiotemporal kinetics in spheroids for fluorochrome-loaded liposomes. Results: Experimental binding distributions of 4 211At-antibodies were considered for treating suspensions of MDA-MB-231 human breast cancer cells. A 2-drug combination reduced the number of 211At decays required by a factor of 1.6 relative to the best single antibody. In another study, 2 radiopharmaceuticals radiolabeled with 195mPt were each distributed lognormally in a hypothetical multicellular cluster. Here, the 2-drug combination required 1.7-fold fewer decays than did either drug alone. Finally, 2 225Ac-labeled drugs that provide different radial distributions within a spheroid require about one half of the disintegrations required by the best single agent. Conclusion: The MIRDcell AI tools determine optimized drug combinations and corresponding molar activities needed to achieve a given SF. This approach could be used to analyze a sample of cells obtained from cell culture, animal, or patient to predict the best combination of drugs for maximum therapeutic effect with the least total disintegrations.

Analysis of chemosensitivity of tumor spheroids exposed to two-dimensional gradient of combination drugs in a hydrogel-based diffusion microfluidic platform

BackgroundLiver cancer stands as a leading cause of cancer-related deaths globally, challenging conventional treatments due to resistance to chemotherapy and targeted therapy. Although frontline medications show initial efficacy, prolonged use often leads to resistance and harm. Current clinical strategies rely on combination therapies, but evaluating their effectiveness remains challenging. ResultsTo address this, we developed a hydrogel-based diffusion microfluidic platform for assessing chemosensitivity. This platform features a hydrogel-filled diffusion layer linked to liquid wells, allowing the creation of drug gradients. Tumor spheroids, cultured on the non-adhesive hydrogel surface, were exposed to single or combination drug gradients. Analysis revealed that drug efficacy, quantified by IC50 values, could be determined from responses to single drug gradients. A novel method was introduced to assess spheroid circularity as a time-invariant index of drug efficacy. Furthermore, exposing spheroids to 2D combination drug gradients allowed intuitive visualization of their responses via a color map. This analysis identified optimal drug combinations, exhibiting superior efficacy to monotherapy. SignificanceThe microfluidic platform enables assessment of synergistic effects and replicates in vivo conditions, enhancing the relevance of test results. By offering a streamlined, fast, and efficient drug screening approach, this platform aims to provide insight into tumor spheroid responses to varying drug combinations, facilitating more effective clinical applications.

Syn-COM: A Multi-Level Predictive Synergy Framework for Innovative Drug Combinations.

Drug prediction and treatment using bioinformatics and large-scale modeling have emerged as pivotal research areas. This study proposes a novel multi-level collaboration framework named Syn-COM for feature extraction and data integration of diseases and drugs. The framework aims to explore optimal drug combinations and interactions by integrating molecular virtuality, similarity clustering, overlap area, and network distance. It uniquely combines the characteristics of Chinese herbal medicine with clinical experience and innovatively assesses drug interaction and correlation through a synergy matrix. Gouty arthritis (GA) was used as a case study to validate the framework's reliability, leading to the identification of an effective drug combination for GA treatment, comprising Tamaricis Cacumen (Si = 0.73), Cuscutae Semen (Si = 0.68), Artemisiae Annuae Herba (Si = 0.62), Schizonepetae Herba (Si = 0.73), Gleditsiae Spina (Si = 0.89), Prunellae Spica (Si = 0.75), and Achyranthis Bidentatae Radix (Si = 0.62). The efficacy of the identified drug combination was confirmed through animal experiments and traditional Chinese medicine (TCM) component analysis. Results demonstrated significant reductions in the blood inflammatory factors IL1A, IL6, and uric acid, as well as downregulation of TGFB1, PTGS2, and MMP3 expression (p < 0.05), along with improvements in ankle joint swelling in GA mice. This drug combination notably enhances therapeutic outcomes in GA by targeting key genes, underscoring the potential of integrating traditional medicine with modern bioinformatics for effective disease treatment.

Synergistic Effects of Artesunate in Combination with Amphotericin B and Miltefosine against Leishmania infantum: Potential for Dose Reduction and Enhanced Therapeutic Strategies.

Leishmaniasis is a tropical infectious disease caused by Leishmania parasites. The disease can be spread by the bite of an infected sand fly. Currently, five chemotherapeutic drugs are available in leishmaniasis treatment. However, these drugs exhibit toxicity and serious adverse effects on infected individuals, necessitating alternative treatment strategies. One such strategy involves using combinations of existing antileishmanial drugs. In this study, we evaluated the interaction between artesunate (AS) and three antileishmanial drugs-amphotericin B (AmB), miltefosine (MF), and paromomycin (PM) against Leishmania infantum. This evaluation marks the first time such an assessment has been conducted. The Chou-Talalay combination index method was employed to analyze the drug interaction. The findings revealed that the interaction between AS and AmB ranged from antagonistic to synergistic, while the interaction between AS and MF showed moderate to strong synergism. In contrast, the interaction between AS and PM resulted in an antagonistic interaction, which differs from the combinations with AmB or MF. This study provides valuable insights for developing novel drug regimens for leishmaniasis treatment, emphasizing the potential of AS and its combination with existing antileishmanial drugs. Further research is necessary to optimize drug combinations and minimize adverse effects, leading to more effective therapeutic outcomes.

Optimizing drug combinations for T-PLL: restoring DNA damage and P53-mediated apoptotic responses

AbstractT-prolymphocytic leukemia (T-PLL) is a mature T-cell neoplasm associated with marked chemotherapy resistance and continued poor clinical outcomes. Current treatments, that is, the CD52-antibody alemtuzumab, offer transient responses, with relapses being almost inevitable without consolidating allogeneic transplantation. Recent more detailed concepts of T-PLL’s pathobiology fostered the identification of actionable vulnerabilities: (1) altered epigenetics, (2) defective DNA damage responses, (3) aberrant cell-cycle regulation, and (4) deregulated prosurvival pathways, including T-cell receptor and JAK/STAT signaling. To further develop related preclinical therapeutic concepts, we studied inhibitors of histone deacetylases ([H]DACs), B-cell lymphoma 2 (BCL2), cyclin-dependent kinase (CDK), mouse double minute 2 (MDM2), and classical cytostatics, using (1) single-agent and combinatorial compound testing in 20 well-characterized and molecularly profiled primary T-PLL (validated by additional 42 cases) and (2) 2 independent murine models (syngeneic transplants and patient-derived xenografts). Overall, the most efficient/selective single agents and combinations (in vitro and in mice) included cladribine, romidepsin ([H]DAC), venetoclax (BCL2), and/or idasanutlin (MDM2). Cladribine sensitivity correlated with expression of its target RRM2. T-PLL cells revealed low overall apoptotic priming with heterogeneous dependencies on BCL2 proteins. In additional 38 T-cell leukemia/lymphoma lines, TP53 mutations were associated with resistance toward MDM2 inhibitors. P53 of T-PLL cells, predominantly in wild-type configuration, was amenable to MDM2 inhibition, which increased its MDM2-unbound fraction. This facilitated P53 activation and downstream signals (including enhanced accessibility of target-gene chromatin regions), in particular synergy with insults by cladribine. Our data emphasize the therapeutic potential of pharmacologic strategies to reinstate P53-mediated apoptotic responses. The identified efficacies and their synergies provide an informative background on compound and patient selection for trial designs in T-PLL.

DIPG-65. NOVEL PATIENT-BASED PHARMACOKINETIC DRUG SCREENING REVEALS GEMCITABINE POTENTIAL IN PEDIATRIC H3K27-ALTERED DIFFUSE MIDLINE GLIOMAS

Abstract BACKGROUND Pediatric H3K27-altered diffuse midline glioma (DMG) remains incurable, with median overall survival of less than one year. Ineffectiveness of chemotherapy is largely attributed to the blood–brain barrier, hindering drug exposure to the tumor. Current drug sensitivity testing use concentrations vastly different from those attainable in the human brain, with optimal combination, timing, and sequence of drugs remaining underexplored. This study aims to systematically select and validate EMA/FDA approved drugs for effective treatment of DMG, based on human pharmacokinetics. METHODS In silico anticancer drug pharmacokinetic (PK) properties were correlated with published DMG cell sensitivities and ranked based on therapeutic index potential. Predictions of brain extracellular fluid time-concentration profiles (AUChumanECF) using plasma PK values of drug candidates, corresponding to human values, were made via physiologically based pharmacokinetic (PBPK) modeling with LeiCNS-PK3.0. In vitro cellular treatment protocols were established that mimic clinically achievable AUChumanECF, to be validated in vivo. RESULTS This PBPK drug screening approach revealed gemcitabine as a highly potent drug for DMG therapy for most cell lines tested (AUCplasma/AUCin_vitro = 337.6±233.0). Replicating a phase I clinical study in pediatric leukemia patients (3 weekly courses of 3600 mg/m2), 3 weekly 6-hour exposures to 12.5 µM.h gemcitabine demonstrated up to 99% cell kill as compared to viability at start of treatment. Notably, the cytotoxic effects of gemcitabine became apparent 3-5 days following drug exposure for most cell lines, and would have been missed using conventional drug screening with direct readouts. CONCLUSIONS This human PBPK-based drug screening approach underscores the significance of precise timing and dosing in elucidating clinically relevant therapeutic effects, highlighting high-dose gemcitabine as a potential therapy for DMG. Cytotoxic effects of gemcitabine on DMG cell viability are cumulative, and undetected when using conventional drug screening. This underlines the importance of investigating real-life human exposure patterns in the context of DMG.

A versatile dilution-treatment-detection microfluidic chip platform for rapid In vitro lung cancer drug combination sensitivity evaluation

Combination drug therapy represents an effective strategy for treating certain drug-resistant and intractable cancer cases. However, determining the optimal combination of drugs and dosages is challenging due to clonal diversity in patients' tumors and the lack of rapid drug sensitivity evaluation methods. Microfluidic technology offers promising solutions to this issue. In this study, we propose a versatile microfluidic chip platform capable of integrating all processes, including dilution, treatment, and detection, for in vitro drug sensitivity assays. This platform innovatively incorporates several modules, including automated discrete drug logarithmic concentration generation, on-chip cell perfusion culture, and parallel drug treatments of cancer cell models. Moreover, it is compatible with microplate readers or high-content imaging systems for swift detection and automated monitoring, simplifying on-chip drug evaluation. Proof of concept is demonstrated by assessing the in vitro potency of two drugs, cisplatin, and etoposide, against the lung adenocarcinoma A549 cell line, under both single-drug and combination treatment conditions. The findings reveal that, compared to conventional microplate approaches with static cultivation, this on-chip automated perfusion bioassays yield comparable IC50 values with lower variation and a 50 % reduction in drug preparation time. This versatile dilution-treatment-detection microfluidic platform offers a promising tool for rapid and precise drug assessments, facilitating in vitro drug sensitivity evaluation in personalized cancer chemotherapy.

Incremental long-term benefit of drug therapies for chronic obstructive pulmonary disease in quality of life but not mortality: a network meta-analysis

IntroductionThe latest evidence revealed that dupilumab, an interleukin-4 (IL-4) and interleukin-13 (IL-13) blocker, significantly reduces the exacerbation risk in patients with chronic obstructive pulmonary disease (COPD). The efficacy of dupilumab compared with conventional inhaled drugs remains incompletely determined. This study aimed to investigate the comparative efficacy of dupilumab and conventional inhaled drugs in patients with stable COPD.Material and methodsThis study retrieved randomised clinical trials (RCTs) with follow-up ≥ 48 weeks on long-acting β-agonists (LABAs), long-acting muscarinic receptor antagonists (LAMAs), inhaled corticosteroids (ICSs), and dupilumab in the PubMed, EMBASE, and Cochrane Central Register of Controlled Trials databases. The information on eligible studies was extracted after the screening. The comparative efficacy of 4 drugs and their combinations in acute exacerbation and mortality was assessed using Bayesian network meta-analysis models.ResultsThis network meta-analysis identified 69 eligible RCTs on 7 classes of drug therapies after stepwise screening and included 125,331 COPD patients. Compared with placebo, the 7 drug interventions significantly reduced the risk of acute exacerbation, and the reduction degree increased with the incremental use of drug classes. ICS/LABA/LAMA/dupilumab was the most effective in decreasing exacerbation risk (OR = 0.561 [95% CI: 0.387–0.810]), followed by ICS/LABA/LAMA (OR = 0.717 [95% CI: 0.626–0.817]). The 7 drug therapies were not significantly associated with a lower risk of death compared to placebo. Nevertheless, ICS/LABA/LAMA/dupilumab is the most likely to be effective in decreasing mortality.ConclusionsThe incremental use of combinations of conventional and novel drugs contributed to the long-term benefits in acute exacerbation but not death in COPD. The optimal drug combination in terms of acute COPD exacerbation was ICS/LABA/LAMA/dupilumab.

Androgen Deprivation Therapy in Prostate Cancer: A Practical Approach to Complex Clinical Scenarios

Androgen deprivation therapy (ADT) has been a cornerstone in the treatment of advanced prostate cancer (PCa) for many years, and continues to form the basis of current therapeutic approaches. However, a recent dramatic upsurge in the number of treatment options available in this area creates a responsibility for healthcare professionals to carefully select optimal drugs and combinations of drugs, to ensure the best outcomes and greatest quality of life for all patients. This requires a thorough understanding of clinical trial data, and an ability to apply this knowledge to everyday clinical practice. This article summarises presentations delivered during a symposium held on 29ᵗʰ November 2023 at the Global Congress on Prostate Cancer (PROSCA) 2023 in Málaga, Spain, where three distinguished speakers described and explored practical approaches to the use of ADT in complex clinical scenarios. Nazareno Suardi, Associate Professor of Urology at the University of Brescia, Italy, described approaches to manage ADT prescription when a patient presents with cardiovascular (CV) risk factors. Matthias Saar, Director and Chairman of the Department of Urology and Uro-oncology at the University Hospital Aachen, Germany, explored whether intermittent ADT remains an alternative in the new era of hormonal therapy. Alison Birtle, Consultant Clinical Oncologist at the Rosemere Cancer Centre, Lancashire Teaching Hospitals NHS Trust, Preston, UK, investigated the importance of drug choice in triplet or doublet therapy in patients with metastatic hormone-sensitive PCa (mHSPC). The meeting was chaired by Antoni Vilaseca, Senior Specialist in the Urology Service at the Hospital Clinic, University of Barcelona, Spain, who introduced the symposium, and also co-ordinated a question and answer session to conclude the meeting.

Advancements and challenges in pharmacokinetic and pharmacodynamic research on the traditional Chinese medicine saponins: a comprehensive review.

Recent research on traditional Chinese medicine (TCM) saponin pharmacokinetics has revealed transformative breakthroughs and challenges. The multicomponent nature of TCM makes it difficult to select representative indicators for pharmacokinetic studies. The clinical application of saponins is limited by their low bioavailability and short half-life, resulting in fluctuating plasma concentrations. Future directions should focus on novel saponin compounds utilizing colon-specific delivery and osmotic pump systems to enhance oral bioavailability. Optimizing drug combinations, such as ginsenosides with aspirin, shows therapeutic potential. Rigorous clinical validation is essential for practical applications. This review emphasizes a transformative era in saponin research, highlighting the need for clinical validation. TCM saponin pharmacokinetics, guided by traditional principles, are in development, utilizing multidisciplinary approaches for a comprehensive understanding. This research provides a theoretical basis for new clinical drugs and supports rational clinical medication.

A Multi-Drug Concentration Gradient Mixing Chip: A Novel Platform for High-Throughput Drug Combination Screening.

Combinatorial drug therapy has emerged as a critically important strategy in medical research and patient treatment and involves the use of multiple drugs in concert to achieve a synergistic effect. This approach can enhance therapeutic efficacy while simultaneously mitigating adverse side effects. However, the process of identifying optimal drug combinations, including their compositions and dosages, is often a complex, costly, and time-intensive endeavor. To surmount these hurdles, we propose a novel microfluidic device capable of simultaneously generating multiple drug concentration gradients across an interlinked array of culture chambers. This innovative setup allows for the real-time monitoring of live cell responses. With minimal effort, researchers can now explore the concentration-dependent effects of single-agent and combination drug therapies. Taking neural stem cells (NSCs) as a case study, we examined the impacts of various growth factors-epithelial growth factor (EGF), platelet-derived growth factor (PDGF), and fibroblast growth factor (FGF)-on the differentiation of NSCs. Our findings indicate that an overdose of any single growth factor leads to an upsurge in the proportion of differentiated NSCs. Interestingly, the regulatory effects of these growth factors can be modulated by the introduction of additional growth factors, whether singly or in combination. Notably, a reduced concentration of these additional factors resulted in a decreased number of differentiated NSCs. Our results affirm that the successful application of this microfluidic device for the generation of multi-drug concentration gradients has substantial potential to revolutionize drug combination screening. This advancement promises to streamline the process and accelerate the discovery of effective therapeutic drug combinations.

Combination of diuretics for acute heart failure: a protocol for a systematic review of randomised clinical trials with network meta-analysis and trial sequential analysis

IntroductionAcute heart failure (AHF) is a critical, costly condition with high mortality rates, affecting millions annually. Despite advances in cardiovascular care, AHF treatment lacks robust evidence. AHF commonly manifests with...

A Novel Approach for Glioblastoma Treatment by Combining Apoptosis Inducers (TMZ, MTX, and Cytarabine) with E.V.A. (Eltanexor, Venetoclax, and A1210477) Inhibiting XPO1, Bcl-2, and Mcl-1.

Adjuvant treatment for Glioblastoma Grade 4 with Temozolomide (TMZ) inevitably fails due to therapeutic resistance, necessitating new approaches. Apoptosis induction in GB cells is inefficient, due to an excess of anti-apoptotic XPO1/Bcl-2-family proteins. We assessed TMZ, Methotrexate (MTX), and Cytarabine (Ara-C) (apoptosis inducers) combined with XPO1/Bcl-2/Mcl-1-inhibitors (apoptosis rescue) in GB cell lines and primary GB stem-like cells (GSCs). Using CellTiter-Glo® and Caspase-3 activity assays, we generated dose-response curves and analyzed the gene and protein regulation of anti-apoptotic proteins via PCR and Western blots. Optimal drug combinations were examined for their impact on the cell cycle and apoptosis induction via FACS analysis, paralleled by the assessment of potential toxicity in healthy mouse brain slices. Ara-C and MTX proved to be 150- to 10,000-fold more potent in inducing apoptosis than TMZ. In response to inhibitors Eltanexor (XPO1; E), Venetoclax (Bcl-2; V), and A1210477 (Mcl-1; A), genes encoding for the corresponding proteins were upregulated in a compensatory manner. TMZ, MTX, and Ara-C combined with E, V, and A evidenced highly lethal effects when combined. As no significant cell death induction in mouse brain slices was observed, we conclude that this drug combination is effective in vitro and expected to have low side effects in vivo.

Reactive Oxygen Species Modulation in the Current Landscape of Anticancer Therapies.

Significance: Reactive oxygen species (ROS) are generated during mitochondrial oxidative metabolism, and are tightly controlled through homeostatic mechanisms to maintain intracellular redox, regulating growth and proliferation in healthy cells. However, ROS production is perturbed in cancers where abnormal accumulation of ROS leads to oxidative stress and genomic instability, triggering oncogenic signaling pathways on one hand, while increasing oxidative damage and triggering ROS-dependent death signaling on the other. Recent Advances: Our review illuminates how critical interactions between ROS and oncogenic signaling, the tumor microenvironment, and DNA damage response (DDR) pathways have led to interest in ROS modulation as a means of enhancing existing anticancer strategies and developing new therapeutic opportunities. Critical Issues: ROS equilibrium exists via a delicate balance of pro-oxidant and antioxidant species within cells. "Antioxidant" approaches have been explored mainly in the form of chemoprevention, but there is insufficient evidence to advocate its routine application. More progress has been made via the "pro-oxidant" approach of targeting cancer vulnerabilities and inducing oxidative stress. Various therapeutic modalities have employed this approach, including direct ROS-inducing agents, chemotherapy, targeted therapies, DDR therapies, radiotherapy, and immunotherapy. Finally, emerging delivery systems such as "nanosensitizers" as radiotherapy enhancers are currently in development. Future Directions: While approaches designed to induce ROS have shown considerable promise in selectively targeting cancer cells and dealing with resistance to conventional therapies, most are still in early phases of development and challenges remain. Further research should endeavor to refine treatment strategies, optimize drug combinations, and identify predictive biomarkers of ROS-based cancer therapies.

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 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.

Perioperative pain optimization in the age of the opioid epidemic.

The opioid epidemic remains a constant and increasing threat to our society with overdoses and overdose deaths rising significantly during the COVID-19 pandemic. Growing evidence suggests a link between perioperative opioid use, postoperative opioid prescribing, and the development of opioid use disorder (OUD). As a result, strategies to better optimize pain management during the perioperative period are urgently needed. The purpose of this review is to summarize the most recent multimodal analgesia (MMA) recommendations, summarize evidence for efficacy surrounding the increased utilization of Enhanced Recovery After Surgery (ERAS) protocols, and discuss the implications for rising use of buprenorphine for OUD patients who present for surgery. In addition, this review will explore opportunities to expand our treatment of complex patients via transitional pain services. There is ample evidence to support the benefits of MMA. However, optimal drug combinations remain understudied, presenting a target area for future research. ERAS protocols provide a more systematic and targeted approach for implementing MMA. ERAS protocols also allow for a more comprehensive approach to perioperative pain management by necessitating the involvement of surgical specialists. Increasingly, OUD patients taking buprenorphine are presenting for surgery. Recent guidance from a multisociety OUD working group recommends that buprenorphine not be routinely discontinued or tapered perioperatively. Lastly, there is emerging evidence to justify the use of transitional pain services for more comprehensive treatment of complex patients, like those with chronic pain, preoperative opioid tolerance, or substance use disorder. Perioperative physicians must be aware of the impact of the opioid epidemic and explore methods like MMA techniques, ERAS protocols, and transitional pain services to improve the perioperative pain experience and decrease the risks of opioid-related harm.

Non-opioid analgesic combinations following total hip arthroplasty (RECIPE): a randomised, placebo-controlled, blinded, multicentre trial

Multimodal postoperative analgesia following total hip arthroplasty is recommended, but the optimal combination of drugs remains uncertain. The aim of the RECIPE trial was to investigate the relative benefit and harm of the different combinations of paracetamol, ibuprofen, and the analgesic adjuvant dexamethasone for treatment of postoperative pain following total hip arthroplasty. The RECIPE trial was a randomised, blinded, placebo-controlled trial conducted at nine Danish hospitals. Adults scheduled for total hip arthroplasty were randomly assigned (1:1:1:1) using a computer-generated list with stratification by site to receive combinations of oral paracetamol 1000 mg every 6 h, oral ibuprofen 400 mg every 6 h, or a single-dose of intravenous dexamethasone 24 mg in the following groups: paracetamol plus ibuprofen, ibuprofen plus dexamethasone, paracetamol plus dexamethasone, and paracetamol plus ibuprofen plus dexamethasone. The primary outcome was 24 h intravenous morphine consumption, analysed in a modified intention-to-treat population, defined as all randomly assigned participants who underwent total hip arthroplasty. The predefined minimal important difference was 8 mg. Safety outcomes included serious and non-serious adverse events within 90 days and 24 h. The trial was registered with ClinicalTrials.gov, NCT04123873. Between March 5, 2020, and Nov 15, 2022, we randomly assigned 1060 participants, of whom 1043 (589 [56%] women and 454 [44%] men) were included in the modified intention-to-treat population. 261 were assigned to paracetamol plus ibuprofen, 262 to ibuprofen plus dexamethasone, 262 to paracetamol plus dexamethasone, and 258 to paracetamol plus ibuprofen plus dexamethasone. Median 24 h morphine consumption was 24 mg (IQR 12-38) in the paracetamol plus ibuprofen group, 20 mg (12-32) in the paracetamol plus dexamethasone group, 16 mg (10-30) in the ibuprofen plus dexamethasone group, and 15 mg (8-26) in the paracetamol plus ibuprofen plus dexamethasone group. The paracetamol plus ibuprofen plus dexamethasone group had a significantly reduced 24 h morphine consumption compared with paracetamol plus ibuprofen (Hodges-Lehmann median difference -6 mg [99% CI -10 to -3]; p<0·0001) and paracetamol plus dexamethasone (-4 mg [-8 to -1]; p=0·0013), however, none of the comparisons showed differences reaching the minimal important threshold of 8 mg. 91 (35%) of 258 participants in the paracetamol plus ibuprofen plus dexamethasone group had one or more adverse events, compared with 99 (38%) of 262 in the ibuprofen plus dexamethasone group, 103 (39%) of 262 in the paracetamol plus dexamethasone group, and 165 (63%) of 261 in the paracetamol plus ibuprofen group. In adults undergoing total hip arthroplasty, a combination of paracetamol, ibuprofen, and dexamethasone had the lowest morphine consumption within 24 h following surgery and the most favourable adverse event profile, with a lower incidence of serious and non-serious adverse events (primarily driven by differences in nausea, vomiting, and dizziness) compared with paracetamol plus ibuprofen. The Novo Nordisk Foundation and Næstved-Slagelse-Ringsted Hospitals' Research Fund.

Machine Learning-Assisted Optimization of Drug Combinations in Zeolite-Based Delivery Systems for Melanoma Therapy.

Two independent artificial neural network (ANN) models were used to determine the optimal drug combination of zeolite-based delivery systems (ZDS) for cancer therapy. The systems were based on the NaY zeolite using silver (Ag+) and 5-fluorouracil (5-FU) as antimicrobial and antineoplastic agents. Different ZDS samples were prepared, and their characterization indicates the successful incorporation of both pharmacologically active species without any relevant changes to the zeolite structure. Silver acts as a counterion of the negative framework, and 5-FU retains its molecular integrity. The data from the A375 cell viability assays, involving ZDS samples (solid phase), 5-FU, and Ag+ aqueous solutions (liquid phase), were used to train two independent machine learning (ML) models. Both models exhibited a high level of accuracy in predicting the experimental cell viability results, allowing the development of a novel protocol for virtual cell viability assays. The findings suggest that the incorporation of both Ag and 5-FU into the zeolite structure significantly potentiates their anticancer activity when compared to that of the liquid phase. Additionally, two optimal AgY/5-FU@Y ratios were proposed to achieve the best cell viability outcomes. The ZDS also exhibited significant efficacy against Escherichia coli (E.coli) and Staphylococcus aureus (S. aureus); the predicted combination ratio is also effective against S.aureus, underscoring the potential of this approach as a therapeutic option for cancer-associated bacterial infections.

Optimizing drug combination and mechanism analysis based on risk pathway crosstalk in pan cancer

Combination therapy can greatly improve the efficacy of cancer treatment, so identifying the most effective drug combination and interaction can accelerate the development of combination therapy. Here we developed a computational network biological approach to identify the effective drug which inhibition risk pathway crosstalk of cancer, and then filtrated and optimized the drug combination for cancer treatment. We integrated high-throughput data concerning pan-cancer and drugs to construct miRNA-mediated crosstalk networks among cancer pathways and further construct networks for therapeutic drug. Screening by drug combination method, we obtained 687 optimized drug combinations of 83 first-line anticancer drugs in pan-cancer. Next, we analyzed drug combination mechanism, and confirmed that the targets of cancer-specific crosstalk network in drug combination were closely related to cancer prognosis by survival analysis. Finally, we save all the results to a webpage for query (http://bio-bigdata.hrbmu.edu.cn/oDrugCP/). In conclusion, our study provided an effective method for screening precise drug combinations for various cancer treatments, which may have important scientific significance and clinical application value for tumor treatment.