Synergistic Combination Research Articles

Hand-in-glove Effect of Piperine in Anti-cancer Formulations

Background: Piperine, an active constituent isolated from Piper nigrum, Piper chaba, Piper sarmentosum, Piper guineense and Piper longum which is used to cure a variety of ailments related to lung, stomach, colon, skin etc. like Alzheimer’s, Parkinson’s disease, ulcerative colitis, cancer, arthritis, inflammatory bowel diseases, diabetes etc. Objective: The objective of the review is to study the synergistic effect of piperine specially in cancer. Methods: Patents and current literature mainly emphasizing the synergistic effect of piperine along with other drugs, especially anticancer drugs was reviewed keeping in mind the scientific novelty and impact. Results: Several synergistic combinations of piperine were explored which can be beneficial for the pharmaceutical industry. Conclusion: This review will assist the researchers in the preparation of novel formulations with improved efficacy cutting the cost of developing new drug molecules.

Towards in-depth profile control using s-MPG synergy with MSRG in fractured-vuggy carbonate reservoirs

In-depth profile control is a crucial technique employed to enhance oil recovery in fractured-vuggy carbonate reservoirs. However, it is a challenge to achieve in-depth profile control. In this paper, two types of organic gel systems, namely s-MPG and MSRG, tailored for fractured-vuggy reservoirs with 140 °C and 22 × 104 mg/L have been developed. FTIR was used to analyze the functional groups of s-MPG and MSRG. Additionally, the quality retention rates of s-MPG and MSRG were assessed using TG-DSC, yielding results of 92.85% and 92.65%, respectively. The dilution rates of s-MPG and MSRG are found to be 18.69% and 26.69%, respectively, demonstrating excellent compatibility and adaptability. The enhancement performance depends on the synergistic effect that the anti-dilution s-MPG effectively separates bottom water, while high-strength MSRG separates the oil layer. Moreover, the EOR performances of s-MPG synergy with MSRG in various types of fractured-vuggy carbonate models were also evaluated. The highest oil recovery of 12% is achieved in fracture network model. Laboratory results indicate that the synergistic combination of s-MPG and MSRG for water plugging in fractured-vuggy carbonate reservoirs results in a more effective enhancement of oil recovery compared to using a single gel system for plugging. Finally, the s-MPG synergy with MSRG has been applied in actual fractured-vuggy carbonate reservoirs. As expected, the water cut of typical well is reduced from 100% to 30% and the increased oil production is 1142 t totally. Therefore, this study presents a novel approach to achieving in-depth profile control by leveraging the synergistic effect of s-MPG with MSRG in fractured-vuggy carbonate reservoirs.

MoS2@Ti3C2Tx Heterostructure: A new negative electrode material for Li-Ion hybrid supercapacitors

Lithium-ion hybrid supercapacitors (LHSCs) demonstrate promising electrochemical performance, including long cyclic stability and a high-power density. However, their limited energy density has restricted the development towards commercialization. In this work, a novel heterostructure (HS) has been introduced based on molybdenum disulfide (MoS2) nanosheets (NSs) anchored on the surface of exfoliated layers of titanium carbide (Ti3C2Tx) MXenes. The prepared negative electrode material named as MoS2@Ti3C2Tx–HS holds substantial promise for advancing LHSCs to enhance energy density. The MoS2@Ti3C2Tx–HS demonstrates outstanding pseudocapacitive performance in a three-electrode system by achieving a capacitance of 1022.7 F g−1 at 1 A g−1. It exhibited a stable cyclic life (5,000 cycles) and retained 97.02 % of its initial capacitance. Theoretical calculations based on density functional theory (DFT) show impressive results to support the experimental work. The calculated density of states (DOS), band structures, adsorption energies (Eads), and diffusion energy demonstrate that MoS2@Ti3C2Tx–HS possesses excellent electronic and adsorption properties. The fabricated MoS2@Ti3C2Tx–HS//CP–LHSCs device shows a high capacitance of 153.75 F g−1 at 2 A g−1, with stable cyclic life (94.5 %) over 10,000 cycles. Furthermore, it shows a notable high energy density of 54.7 Wh kg−1 at a power density of 1,601.3 W kg−1. The prepared MoS2@Ti3C2Tx–HS electrode, with its synergistic combination, stands out as an auspicious material, showing remarkable electrochemical performance and paving the way for LHSCs.

The Role of Probiotics in Managing Metabolic-Associated Fatty Liver Disease: An Updated Review

Metabolic-associated fatty liver disease (MAFLD) has become a very significant health problem worldwide, characterized by hepatic steatosis and systemic metabolic disturbances. Grasping the complex interaction between the intestine and liver, referred to as the gut-liver axis, is crucial to decipher the pathogenesis of MAFLD and identify potential therapeutic targets. Probiotics, which are live microorganisms offering health benefits, have garnered attention for their potential in treating MAFLD. A literature search in PubMed and Google Scholar using "MAFLD," "NAFLD," "probiotics," and "gut microbiota" yielded 642 studies; 32 clinical trials met the inclusion criteria for the final analysis. This review encapsulates the current knowledge of MAFLD pathophysiology, emphasizing the role of the gut-liver axis, and assesses the clinical evidence supporting probiotic treatments for MAFLD. The mechanisms of action of probiotics are explored, including their ability to modulate gut microbiota composition, enhance epithelial barrier function, and influence the immune response. Various randomized controlled trials have shown that probiotics are effective in enhancing several aspects of MAFLD, such as liver enzymes, lipid profiles, body mass index, insulin sensitivity, or even histological efficacy. However, mixed results have been reported in some clinical trials, highlighting the need for further research to optimize probiotic interventions. Future investigations should focus on standardized protocols, personalized approaches and exploring synergistic combinations with other therapeutic modalities to maximize the potential of probiotics in the treatment of MAFLD.

Cost-benefit analysis in UK hotels: A hybrid SOCP-MCDM approach

Performance evaluation has been an important topic of concern for tourism industry practitioners as well as academic researchers, and its investigation in the UK hotel sector is paramount because this industry has been experiencing a higher level of competition. The present study contributes to the previous literature on hotel performance evaluation in general by proposing an innovative hybrid method combining the second-order cone programming (SOCP) method and multi-criteria decision-making (MCDM) method to estimate the performance of the UK hotel sector. The innovation lies in the synergistic combination of SOCP and MCDM methodologies, enabling a comprehensive assessment of hotel performance by managing a non-linear optimisation. Overall, this hybrid method benefits from the ability to be more flexible in addressing complex operational issues and provide more accurate results. This research provides a cost-benefit analysis within the proposed method, suggesting important policy implications in the tourism industry.

Bioinspired synergy strategy based on the integration of nanozyme into a molecularly imprinted polymer for improved enzyme catalytic mimicry and selective biosensing

Nanozymes offer many advantages such as good stability and high catalytic activity, but their selectivity is lower than that of enzymes. This is because most of enzymes have a protein component (apoenzyme) for substrate affinity to enhance selectivity and a non-protein element (coenzyme) for catalytic activity to improve sensitivity. The synergy between molecularly imprinted polymers (MIPs) and nanozymes can mimic natural enzymes, with MIP acting as the apoenzyme and nanozyme as the coenzyme. Despite researchers' attempts to associate MIPs with nanozymes, the full potential of this combination remains not well explored. This study addresses this gap by integrating Fe3O4-Lys-Cu nanozymes with peroxidase-like catalytic activities within appropriate MIPs for L-DOPA and dopamine. The catalytic performance of the nanozyme was improved by the presence of Cu in Fe3O4-Lys-Cu and further enhanced by MIP. Indeed, the exploration of the pre-concentration property of MIP has increased twenty-fold the catalytic activity of the nanozyme. Moreover, this synergistic combination facilitated the template removal process during MIP production by reducing the extraction time from several hours to just 1 min thanks to the addition of co-substrates which trigger the reaction with nanozyme and release the template. Overall, the synergistic combination of MIPs and nanozymes offers a promising avenue for the design of artificial enzymes.

Enhanced Cytotoxicity of Sorafenib in Hepatocellular Carcinoma through Synergistic Combination with Naringenin: A Molecular and Cellular Perspective

Enhanced Cytotoxicity of Sorafenib in Hepatocellular Carcinoma through Synergistic Combination with Naringenin: A Molecular and Cellular Perspective

Process optimization on kesterite-based ceramics for enhancing their thermoelectric performances assisted by active machine learning approach: A tool for metal-sulfide ceramics development

The thermal process parameters are crucial in metal-sulfides ceramics as they affect significantly the resulting physico-chemical properties. In the present work, we investigated the sintering effect in the kesterite Cu2.125Zn0.875SnS4 on its structural, microstructural, and thermoelectric (TE) properties to highlight the non-negligible contribution of the thermal process often ignored in metal-sulfide ceramics. For this purpose, we developed an approach combining data science with the conventional material experiment/theory approach which can be used as a tool to shortcut the time-consuming steps of TE material optimization. We confirmed that the optimization and control of the densification process is critical in unravelling the highest potential on metal sulfide TE ceramics with a non-negligible increase of its zT up to 60 %. We propose a scientific tool, the synergic combination of active machine learning with conventional chemistry/theory approaches, to either identify the most proficient sintering process as well as the process to avoid the degradation of the metal-sulfide ceramic properties and thus in a shorten number of experiments. This approach can be extended not only to other metal-sulfide ceramics for thermoelectricity but also to other research fields.

Integrating Generative AI and IoT for Sustainable Smart Tourism Destinations

This paper aims to develop a groundbreaking approach to fostering inclusive smart tourism destinations by integrating generative artificial intelligence (Gen AI) with natural language processing (NLP) and the Internet of Things (IoT) into an intelligent platform that supports tourism decision making and travel planning in smart tourism destinations. The acquisition of this new technology was conducted using Agile methodology through requirements analysis, system architecture analysis and design, implementation, and user evaluation. The results revealed that the synergistic combination of these technologies was organized into three tiers. The system provides information, including place names, images, descriptive text, and an audio option for users to listen to the information, supporting tourists with disabilities. Employing advanced AI algorithms alongside NLP, developed systems capable of generating predictive analytics, personalized recommendations, and conducting real-time, multilingual communication with tourists. This system was implemented and evaluated in Suphan Buri and Ayutthaya, UNESCO World Heritage sites in Thailand, with 416 users participating. The results showed that system satisfaction was influenced by (1) the tourism experience, (2) tourism planning and during-trip factors (attention, interest, and usage), and (3) emotion. The relative Chi-square (χ2/df) of 1.154 indicated that the model was suitable. The Comparative Fit Index (CFI) was 0.990, the Goodness-of-Fit Index (GFI) was 0.965, and the model based on the research hypothesis was consistent with the empirical data. This paper contributions significant advancements in the field of smart tourism by demonstrating the integration of Gen AI, NLP, and the IoT and offering practical solutions and theoretical insights that enhance accessibility, personalization, and environmental sustainability in tourism.

Enhanced photocatalysis by defect-engineered CeO2 with sulfite activation under visible light irradiation

Sulfite-based advanced oxidation processes (s-AOPs) have recently garnered significant attention due to their ability to generate highly oxidative sulfate radicals and eliminate contaminants from water. A state-of-the-art platform has been developed through the synergistic combination of s-AOP and photocatalysis under visible light as weak as ambient indoor daylight. This platform achieved an enhanced degradation efficiency for defect-engineered CeO2 nanocubes by using sodium sulfite instead of SO2, resulting in a synergy factor of up to 88%. The reaction pathways, intermediates, ion leaching and the possible influence of aqueous conditions during the methyl orange degradation have been investigated. The CeO2/Na2SO3/vis-light platform exhibits promising prospect as a system to enhance conventional s-AOP in wastewater treatment or disinfection. It demonstrates excellent performance in terms of pH sensitivity, anion influence, and resistance to leaching. This highlights its significant potential for practical applications and future process scale-up.

Damage-programmable design of metamaterials achieving crack-resisting mechanisms seen in nature

The fracture behaviour of artificial metamaterials often leads to catastrophic failures with limited resistance to crack propagation. In contrast, natural materials such as bones and ceramics possess microstructures that give rise to spatially controllable crack path and toughened material resistance to crack advances. This study presents an approach that is inspired by nature’s strengthening mechanisms to develop a systematic design method enabling damage-programmable metamaterials with engineerable microfibers in the cells that can spatially program the micro-scale crack behaviour. Machine learning is applied to provide an effective design engine that accelerate the generation of damage-programmable cells that offer advanced toughening functionality such as crack bowing, crack deflection, and shielding seen in natural materials; and are optimised for a given programming of crack path. This paper shows that such toughening features effectively enable crack-resisting mechanisms on the basis of the crack tip interactions, crack shielding, crack bridging and synergistic combinations of these mechanisms, increasing up to 1,235% absorbed fracture energy in comparison to conventional metamaterials. The proposed approach can have broad implications in the design of damage-tolerant materials, and lightweight engineering systems where significant fracture resistances or highly programmable damages for high performances are sought after.

Multifunctional bio-nanocomposite films integrated with essential oils@metal−phenolic network nanocapsules for durable fruit preservation

Food spoilage exacerbates global hunger and poverty, necessitating urgent advancements in food shelf life extension methodologies. However, balancing antibacterial efficacy for food preservation with human and environmental safety remains a significant challenge. Natural essential oils (EOs), known for their potent antibacterial and antioxidant properties, offer eco-friendly alternatives, yet their high volatility and instability limit practical applications. Herein, we conducted the encapsulation of EOs within biocompatible metal phenolic networks (MPNs) to create EOs@MPN nanocapsules. Subsequently, these nanocapsules were integrated into bio-nanocomposite films composed of natural soy protein isolate (SPI) and carboxymethyl cellulose (CMC). The resulting films exhibited robust mechanical properties (Tensile Strength >10 MPa) and significantly enhanced antioxidant activity (7-fold higher than pure films). Importantly, the synergistic combination of EOs and MPNs conferred enhanced antibacterial efficacy. Safety assessments confirmed the bio-nanocomposite films' high biodegradability (> 90 %) and negligible cytotoxicity, ensuring environmental sustainability and human health safety. In practical applications, the bio-nanocomposite films effectively delayed the surface browning of fresh-cut fruits for up to 48 h, demonstrating a pronounced synergistic antioxidative effect against oxidation. Moreover, tomatoes and blueberries packaged with the bio-nanocomposite films still maintained freshness for up to 12 days, offering promising strategies for extending the shelf life of perishable fruits. These findings underscore the potential of EOs@MPN-based bio-nanocomposite films as sustainable solutions for food preservation and highlight their practical viability in mitigating food spoilage and enhancing food security globally.

Nutraceutical formulation based on a synergic combination of melatonin and palmitoylethanolamide for the management of allergic events.

The management of allergic events is a growing global health issue, especially in industrialized countries. This disease is an immune-mediated process, regulated by the interaction of IgE with an allergen, resulting in mast cell activation, which concerns the release of several immune-inflammatory modulators, i.e., histamine, β-hexosaminidase, COX-2, IL-6, and TNF-α, responsible for the main allergic-reaction associated symptoms. The aim of the present study was the efficacy evaluation of an alternative remedy, an innovative nutraceutical formulation (NF) based on the synergic combination of melatonin (MEL) and palmitoylethanolamide (PEA) for the prevention and treatment of immune disease. At first, the intestinal bioaccessibility of PEA and MEL in NF was assessed at 1.6 and 36%, respectively. Then the MEL and PEA ability to modulate the release of immune-inflammatory modulators in the human mast cell line (HMC-1.2) at their bioaccessible concentration was investigated. Our results underline that NF treatment was able to reduce COX-2 mRNA transcription levels (-30% vs. STIM, p < 0.0001) in stimulated HMC-1.2 and to contract COX-2 enzymatic activity directly (IC50: 152 μg/mL). Additionally, NF showed valuable ability in reducing histamine and β-hexosaminidase release in stimulated HMC-1.2, as well as in decreasing TNF-α and IL-6 mRNA transcription levels and protein production.

Investigating the synergistic antibacterial effects of chlorogenic and p-coumaric acids on Shigella dysenteriae

Chlorogenic acid (CGA) is a well-known plant secondary metabolite exhibiting multiple physiological functions. The present study focused on screening for synergistic antibacterial combinations containing CGA. The combination of CGA and p-coumaric acid (pCA) exhibited remarkably enhanced antibacterial activity compared to that when administering the treatment only. Scanning electron microscopy revealed that a low-dose combination treatment could disrupt the Shigella dysenteriae cell membrane. A comprehensive analysis using nucleic acid and protein leakage assay, conductivity measurements, and biofilm formation inhibition experiments revealed that co-treatment increased the cell permeability and inhibited the biofilm formation substantially. Further, the polyacrylamide protein- and agarose gel-electrophoresis indicated that the proteins and DNA genome of Shigella dysenteriae severely degraded. Finally, the synergistic bactericidal effect was established for fresh-cut tomato preservation. This study demonstrates the remarkable potential of strategically selecting antibacterial agents with maximum synergistic effect and minimum dosage exhibiting excellent antibacterial activity in food preservation.

DDSBC: A Stacking Ensemble Classifier-Based Approach for Breast Cancer Drug-Pair Cell Synergy Prediction.

Breast cancer (BC) ranks as a leading cause of mortality among women worldwide, with incidence rates continuing to rise. The quest for effective treatments has led to the adoption of drug combination therapy, aiming to enhance drug efficacy. However, identifying synergistic drug combinations remains a daunting challenge due to the myriad of potential drug pairs. Current research leverages machine learning (ML) and deep learning (DL) models for drug-pair synergy prediction and classification. Nevertheless, these models often underperform on specific cancer types, including BC, as they are trained on data spanning various cancers without any specialization. Here, we introduce a stacking ensemble classifier, the drug-drug synergy for breast cancer (DDSBC), tailored explicitly for BC drug-pair cell synergy classification. Unlike existing models that generalize across cancer types, DDSBC is exclusively developed for BC, offering a more focused approach. Our comparative analysis against classical ML methods as well as DL models developed for drug synergy prediction highlights DDSBC's superior performance across test and independent datasets on BC data. Despite certain metrics where other methods narrowly surpass DDSBC by 1-2%, DDSBC consistently emerges as the top-ranked model, showcasing significant differences in scoring metrics and robust performance in ablation studies. DDSBC's performance and practicality position it as a preferred choice or an adjunctive validation tool for identifying synergistic or antagonistic drug pairs in BC, providing valuable insights for treatment strategies.

Synergistic combination effect of the PCA-1/ALKBH3 inhibitor HUHS015 on prostate cancer drugs in vitro and in vivo.

Prostate cancer antigen-1/ALKBH3, a DNA/RNA demethylase of 3-methylcytosine, 1-methyladenine (1-meA), and 6-meA, was found in prostate cancer as an important prognostic factor. Additionally, 1-meA has been associated with other cancers. The ALKBH3 inhibitor HUHS015 was found to be effective against prostate cancer both in vitro and in vivo. Herein, we investigated the effect of HUHS015 in combination with drugs for prostate cancer approved in Japan (including bicalutamide, cisplatin, mitoxantrone, prednisolone, ifosfamide, tegafur/uracil, docetaxel, dacarbazine, and estramustine) by treating DU145 cells with around IC50 value concentrations of these drugs for 3 days. Additionally, the cells were observed for additional 9 days after drug removal. Combination treatment with dacarbazine, estramustine, tegafur/uracil, and HUHS015 showed a slight additive effect after 3 days. After drug washout of them and mitoxantrone, the combined effects and levels were enhanced and sustained, although the effects of each treatment alone declined. HUHS015 combined with cisplatin or docetaxel elicited synergistic and sustained effects. In vivo, combining HUHS015 and docetaxel, the first chemotherapeutic agent for castration-resistant prostate cancer, showed notable effects in the DU145 xenograft model. In conclusion, HUHS015 exhibited a synergistic effect with docetaxel and drugs acting on DNA in vitro, even after drug removal. Since cancer chemotherapy is typically administered during rest periods due to its high toxicity, combining it with an ALKBH3 inhibitor could be a promising strategy for enhancing cancer treatment, as it can elicit an additive effect during treatment, allowing dosage reduction, and synergistically sustain the effect after drug washout during rest periods.

Efficient adsorption of hydroxychloroquine by carboxylated lignin-based sponge: Quantification of the adsorption contribution of interactions

Hydroxychloroquine (HCQ) as an antiviral pharmaceutical has been widely detected in the aquatic environment. Adsorption is an effective method for removal of HCQ, but the quantitative study of the adsorption interactions is unclear. In this study, a carboxylated lignin-based sponge (PLC) was fabricated, and the adsorption contributions of electrostatic attraction (EA), hydrogen bond (HB), and π-π electron donor–acceptor (π-π EDA) interactions were quantitatively investigated. The results showed that PLC exhibited excellent adsorption performance under wide pH range (4.0 < pH < 11.0), with the highest adsorption capacity of 0.165 mmol/g. Inhibitor and molecular probe experiments showed that EA and HB were the main contributors to adsorption at neutral and basic pH ranges, respectively. A linear correlation model of the carboxyl groups of lignin-based sponges with the adsorption capacity of EA, HB, and π-π EDA interactions of HCQ (R2 > 0.822) and three molecular probes (R2 > 0.749) was established, respectively. The optimal adsorption capacity was achieved by the synergistic combination of the deprotonated carboxyl of PLC with the HB of Ka1 amino, EA of Ka2 amino, and the π-π EDA of the quinoline ring. This study provided a new strategy for the quantitative construction of structure–activity relationships of adsorbents.

Deep near infrared light-excited stable synergistic photodynamic and photothermal therapies based on P-IR890 nano-photosensitizer constructed via a non-cyanine dye

The cyanine dyes represented by IR780 can achieve synergistic photodynamic therapy (PDT) and photothermal therapy (PTT) under the stimulation of near-infrared (NIR) light (commonly 808 nm). Unfortunately, the stability of NIR-excited cyanine dyes is not satisfactory. These cyanine dyes can be attacked by self-generated reactive oxygen species (ROS) during PDT processes, resulting in structural damage and rapid degradation, which is fatal for phototherapy. To address this issue, a novel non-cyanine dye (IR890) was elaborately designed and synthesized by our team. The maximum absorption wavelength of IR890 was located in the deep NIR region (ca. 890 nm), which was beneficial for further improving tissue penetration depth. Importantly, IR890 exhibited good stability when continuously illuminated by deep NIR light. To improve the hydrophilicity and biocompatibility, the hydrophobic IR890 dye was grafted onto the side chain of hydrophilic polymer (POEGMA-b-PGMA-g-CCH) via click chemistry. Then, the synthesized POEGMA-b-PGMA-g-IR890 amphiphilic polymer was utilized to prepare P-IR890 nano-photosensitizer via self-assembly method. Under irradiation with deep NIR light (850 nm, 0.5 W/cm2, 10 min), the dye degradation rate of P-IR890 was less than 5%. However, IR780 was almost completely degraded with the same light output power density and irradiation duration. In addition, P-IR890 could stably generate a large number of ROS and heat at the same time. It was rarely reported that the stable synergistic combination therapy of PDT and PTT could be efficiently performed by a single photosensitizer via irradiation with deep NIR light. P-IR890 exhibited favorable anti-tumor outcomes through apoptosis pathway. Therefore, the P-IR890 could provide a new insight into the design of photosensitizers and new opportunities for synergistic combination therapy of PDT and PTT.

MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.

Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships between drugs remains a highly challenging task. This paper proposes a novel deep learning model MMFSyn based on multimodal drug data combined with cell line features. Firstly, to ensure the full expression of drug molecular features, multiple modalities of drugs, including Morgan fingerprints, atom sequences, molecular diagrams, and atomic point cloud data, are extracted using SMILES. Secondly, for different modal data, a Bi-LSTM, gMLP, multi-head attention mechanism, and multi-scale GCNs are comprehensively applied to extract the drug feature. Then, it selects appropriate omics features from gene expression and mutation omics data of cancer cell lines to construct cancer cell line features. Finally, these features are combined to predict the synergistic anti-cancer drug combination effect. The experimental results verify that MMFSyn has significant advantages in performance compared to other popular methods, with a root mean square error of 13.33 and a Pearson correlation coefficient of 0.81, which indicates that MMFSyn can better capture the complex relationship between multimodal drug combinations and omics data, thereby improving the synergistic drug combination prediction.

Computational Modeling of Drug Response Identifies Mutant-Specific Constraints for Dosing panRAF and MEK Inhibitors in Melanoma.

This study explores the potential of pre-clinical in vitro cell line response data and computational modeling in identifying the optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential. In a drug combination screen of 43 melanoma cell lines, we identified specific dosage landscapes of panRAF and MEK inhibitors for NRAS vs. BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma (mean Bliss score of 0.27 in NRAS vs. 0.1 in BRAF mutants). Computational modeling and follow-up molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated the in vivo translatability of in vitro dose-response maps by predicting tumor growth in xenografts with high accuracy in capturing cytostatic and cytotoxic responses. We analyzed the pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients. Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range. Overall, this work presents a framework to aid dose selection in drug combinations.