Fully bio-based intumescent flame retardant hybrid: A green strategy towards reducing fire hazard and improving degradation of polylactic acid.

Polylactic acid (PLA) is a promising renewable polymer material with excellent biodegradability and good mechanical properties. However, the easy flammability and slow natural degradation limited its further applications, especially in high-security fields. In this work, a fully bio-based intumescent flame-retardant system was designed to reduce the fire hazard of PLA. Firstly, arginine (Arg) and phytic acid (PA) were combined through electrostatic ionic interaction, followed by the introduction of starch as a carbon source, namely APS. The UL-94 grade of PLA/APS composites reached V-0 grade by adding 3 wt% of APS and exhibited excellent anti-dripping performance. With APS addition increasing to 7 wt%, LOI value increased to 26 % and total heat release decreased from 58.4 (neat PLA) to 51.1 MJ/m2. Moreover, the addition of APS increased its crystallinity up to 83.5 % and maintained the mechanical strength of pristine PLA. Noteworthy, APS accelerated the degradation rate of PLA under submerged conditions. Compared with pristine PLA, PLA/APS showed more apparent destructive network morphology and higher mass and Mn loss, suggesting effective degradation promotion. This work provides a full biomass modification strategy to construct renewable plastic with both good flame retardancy and high degradation efficiency.

Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology.

Understanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the perspectives of leveraging omics-derived bioinformatics estimates to inform QSP models and circumvent the challenges of model calibration and validation in immuno-oncology.

From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling.

Virtual patients and digital patients/twins are two similar concepts gaining increasing attention in health care with goals to accelerate drug development and improve patients' survival, but with their own limitations. Although methods have been proposed to generate virtual patient populations using mechanistic models, there are limited number of applications in immuno-oncology research. Furthermore, due to the stricter requirements of digital twins, they are often generated in a study-specific manner with models customized to particular clinical settings (e.g., treatment, cancer, and data types). Here, we discuss the challenges for virtual patient generation in immuno-oncology with our most recent experiences, initiatives to develop digital twins, and how research on these two concepts can inform each other.

ALKBH5 facilitates the progression of skin cutaneous melanoma via mediating ABCA1 demethylation and modulating autophagy in an m6A-dependent manner.

Background: N6-methyladenosine (m6A) is the most common and abundant mRNA modification, playing an essential role in biological processes and tumor development. However, the role of m6A methylation in skin cutaneous melanoma (SKCM) is not yet clear. This study analyzed the expression of m6A-related functional genes in SKCM and aimed to explore the key demethylase ALKBH5 mediated m6A modification and its potential mechanism in human SKCM. Methods: Based on public databases, the m6A-related gene expression landscape in SKCM was portrayed. MeRIP-Seq and RNA-Seq were used to recognize the downstream target of ALKBH5. In vivo and in vitro functional phenotype and rescue functional experiments were performed to explore the mechanism of the ALKBH5-m6A-ABCA1 axis in SKCM. Results: We found ALKBH5 upregulated in SKCM, associated with poor prognosis. ALKBH5 can promote melanoma cell proliferation, colony formation, migration, and invasion and inhibit autophagy in vitro, facilitating tumor growth and metastasis in vivo. We identified ABCA1, a membrane protein that assists cholesterol efflux, as a downstream target of ALKBH5-mediated m6A demethylation. Finally, our data demonstrated that ALKBH5 promoted SKCM via mediating ABCA1 downregulation by reducing ABCA1 mRNA stability in an m6A-dependent manner. Conclusion: Our findings exhibited the functional value of the key demethylase ALKBH5 mediated m6A modification in the progression of SKCM, suggesting the ALKBH5-m6A-ABCA1 axis as a potential therapeutic target in SKCM.

Markerless gait analysis through a single camera and computer vision.

The assessment of gait performance using quantitative measures can yield crucial insights into an individual's health status. Recently, computer vision-based human pose estimation has emerged as a promising solution for markerless gait analysis, as it allows for the direct extraction of gait parameters from videos. This study aimed to compare the lower extremity kinematics and spatiotemporal gait parameters obtained from a single-camera-based markerless method with those acquired from a marker-based motion tracking system across a healthy population. Additionally, we investigated the impact of camera viewing angles and distances on the accuracy of the markerless method. Our findings demonstrated a robust correlation and agreement (Rxy > 0.75, Rc > 0.7) between the markerless and marker-based methods for most spatiotemporal gait parameters. We also observed strong correlations (Rxy > 0.8) between the two methods for hip flexion/extension, knee flexion/extension, hip abduction/adduction, and hip internal/external rotation. Statistical tests revealed significant effects of viewing angles and distances on the accuracy of the identified gait parameters. While the markerless method offers an alternative for general gait analysis, particularly when marker use is impractical, its accuracy for clinical applications remains insufficient and requires substantial improvement. Future investigations should explore the potential of the markerless system to measure gait parameters in pathological gaits.

Lithiophilic Covalent Organic Framework as Anode Coating for High-Performance Lithium Metal Batteries.

The growth of disorganized lithium dendrites and weak solid electrolyte interphase greatly impede the practical application of lithium metal batteries. Herein, we designed and synthesized a new kind of stable polyimide covalent organic frameworks (COFs), which have a high density of well-aligned lithiophilic quinoxaline and phthalimide units anchored within the uniform one-dimensional channels. The COFs can serve as an artificial solid electrolyte interphase on lithium metal anode, effectively guiding the uniform deposition of lithium ions and inhibiting the growth of lithium dendrites. The unsymmetrical Li||COF-Cu battery exhibits a Coulombic efficiency of 99 % at a current density of 0.5 mA cm-2 , which can be well retained up to 400 cycles. Meanwhile, the Li-COF||LFP full cell shows a Coulombic efficiency over 99 % at a charge of 0.3 C. And its capacity can be well maintained up to 91 % even after 150 cycles. Therefore, the significant electrochemical cycling stability illustrates the feasibility of employing COFs in solving the disordered deposition of lithium ions in lithium metal batteries.

Inhibition of phosphatidylinositol 3-kinase catalytic subunit alpha by miR-203a-3p reduces hypertrophic scar formation via phosphatidylinositol 3-kinase/AKT/mTOR signaling pathway.

Background: Hypertrophic scar (HS) is a common fibroproliferative skin disease that currently has no truly effective therapy. Given the importance of phosphatidylinositol 3-kinase catalytic subunit alpha (PIK3CA) in hypertrophic scar formation, the development of therapeutic strategies for endogenous inhibitors against PIK3CA is of great interest. Here, we explored the molecular mechanisms underlying the protective effects of miR-203a-3p (PIK3CA inhibitor) against excessive scar. Methods: Bioinformatic analysis, immunohistochemistry, immunofluorescence, miRNA screening and fluorescence in situ hybridization assays were used to identify the possible pathways and target molecules mediating HS formation. A series of in vitro and in vivo experiments were used to clarify the role of PIK3CA and miR-203a-3p in HS. Mechanistically, transcriptomic sequencing, immunoblotting, dual-luciferase assay and rescue experiments were executed. Results: Herein, we found that PIK3CA and the phosphatidylinositol 3-kinase (PI3K)/AKT/mTOR pathway were upregulated in scar tissues and positively correlated with fibrosis. We then identified miR-203a-3p as the most suitable endogenous inhibitor of PIK3CA. miR-203a-3p suppressed the proliferation, migration, collagen synthesis and contractility as well as the transdifferentiation of fibroblasts into myofibroblasts in vitro, and improved the morphology and histology of scars in vivo. Mechanistically, miR-203a-3p attenuated fibrosis by inactivating the PI3K/AKT/mTOR pathway by directly targeting PIK3CA. Conclusions: PIK3CA and the PI3K/AKT/mTOR pathway are actively involved in scar fibrosis and miR-203a-3p might serve as a potential strategy for hypertrophic scar therapy through targeting PIK3CA and inactivating the PI3K/AKT/mTOR pathway.

Factors Affecting Workers' Mental Stress in Handover Activities During Human-Robot Collaboration.

OBJECTIVE: This study investigated the effects of different approach directions, movement speeds, and trajectories of a co-robot's end-effector on workers' mental stress during handover tasks. BACKGROUND: Human-robot collaboration (HRC) is gaining attention in industry and academia. Understanding robot-related factors causing mental stress is crucial for designing collaborative tasks that minimize workers' stress. METHODS: Mental stress in HRC tasks was measured subjectively through self-reports and objectively through galvanic skin response (GSR) and electromyography (EMG). Robot-related factors including approach direction, movement speed, and trajectory were analyzed. RESULTS: Movement speed and approach direction had significant effects on subjective ratings, EMG, and GSR. High-speed and approaching from one side consistently resulted in higher fear, lower comfort, and predictability, as well as increased EMG and GSR signals, indicating higher mental stress. Movement trajectory affected GSR, with the sudden stop condition eliciting a stronger response compared to the constrained trajectory. Interaction effects between speed and approach direction were observed for "surprise" and "predictability" subjective ratings. At high speed, approach direction did not significantly differ, but at low speeds, approaching from the side was found to be more surprising and unpredictable compared to approaching from the front. CONCLUSION: The mental stress of workers during HRC is lower when the robot's end effector (1) approaches a worker within the worker's field of view, (2) approaches at a lower speed, or (3) follows a constrained trajectory. APPLICATION: The outcome of this study can serve as a guide to design HRC tasks with a low level of workers' mental stress.

Exploring the impact of human-robot interaction on workers' mental stress in collaborative assembly tasks.

Advances in robotics have contributed to the prevalence of human-robot collaboration (HRC). However, working and interacting with collaborative robots in close proximity can be psychologically stressful. Therefore, understanding the impacts of human-robot interaction (HRI) on mental stress is crucial for enhancing workplace well-being. To this end, this study investigated how the HRI factors - presence, complexity, and modality - affect the psychological stress of workers. We employed both the NASA-Task Load Index for subjective assessment and physiological metrics including galvanic skin responses, electromyography, and heart rate for objective evaluation. An experimental setup was implemented in which human operators worked together with a collaborative robot on Lego assembly tasks, using different interaction paradigms including pressing buttons, showing hand gestures, and giving verbal commands. The results revealed that the introduction of interactions during HRC helped reduce mental stress and that complex interactions resulted in higher mental stress than simple interactions. Meanwhile, using hand gestures led to significantly higher mental stress than pressing buttons and verbal commands. The findings provided practical insights for mitigating mental stress in the workplace and promoting wellness in the era of HRC.

Eliciting the antitumor immune response with a conditionally activated PD-L1 targeting antibody analyzed with a quantitative systems pharmacology model.

Conditionally activated molecules, such as Probody therapeutics (PbTx), have recently been investigated to improve antitumoral response while reducing systemic toxicity. PbTx are engineered to be proteolytically activated by proteases that are preferentially active locally in the tumor microenvironment (TME). Here, we perform an exploratory study using our recently published quantitative systems pharmacology model, previously validated for other drugs, to evaluate the effectiveness and targeting specificity of an anti-PD-L1 PbTx compared to the non-modified antibody. We have informed the model using the PbTx dynamics and pharmacokinetics published in the literature for anti-PD-L1 in patients with triple-negative breast cancer (TNBC). Our results suggest masking of the antibody slightly decreases its efficacy, while increasing the localization of active therapeutic component in the TME. We also perform a parameter optimization for the PbTx design and drug dosing regimens to maximize the response rate. Although our results are specific to the case of TNBC, our findings are generalizable to any conditionally activated PbTx molecule in solid tumors and suggest that design of a highly effective and selective PbTx is feasible.

Cost-effectiveness analysis of insecticide ban aimed at preventing Parkinson's disease in Central California.

BACKGROUND: Our study assessed whether banning specific insecticides to reduce the PD burden in three Central California (CA) counties is cost-effective. METHOD: We applied a cost-effectiveness analysis using a cohort-based Markov model to estimate the impact and costs of banning seven insecticides that were previously associated with PD in these counties as well as mixture exposures to some of these pesticides. We relied for our estimations on the cohort of 65- and 66-year-olds living in these counties who were unaffected by PD at baseline in 2020 and projected their incidence, costs, and reduction in quality-adjusted-life-years (QALY) loss due to developing PD over a 20-year period. We included a shiny app for modeling different scenarios (https://sherlockli.shinyapps.io/pesticide_pd_economics_part_2/). RESULTS: According to our scenarios, banning insecticides to reduce the occurrence of PD in three Central CA counties was cost-effective relative to not banning insecticides. In the worst-case scenario of exposure to a single pesticide, methomyl, versus none would result in an estimated 205 (95 % CI: 75, 348) additional PD cases or 12 % (95 % CI: 4 %, 20 %) increase in PD cases over a 20-year period based on residential proximity to pesticide applications. The increase in PD cases due to methomyl would increase health-related costs by $72.0 million (95 % CI: $5.5 million, $187.4 million). Each additional PD patient due to methomyl exposure would incur $109,327 (95 % CI, $5554, $347,757) in costs per QALY loss due to PD. Exposure to methomyl based on workplace proximity to pesticide applications generated similar estimates. The highest PD burden and associated costs would be incurred from exposure to multiple pesticides simultaneously. CONCLUSION: Our study provides an assessment of the cost-effectiveness of banning specific insecticides to reduce PD burden in terms of health-related QALYs and related costs. This information may help policymakers and stakeholders to make decisions concerning the regulation of pesticides.

Novel approach for enhancing skin allograft survival by bioadhesive nanoparticles loaded with rapamycin.

Skin graft rejection is a significant challenge in skin allografts for skin defects, particularly in extensive burn injury patients when autografts are insufficient. Enhancing the survival duration of allogeneic skin grafts can improve the success rate of subsequent autologous skin grafting, thereby promoting the therapeutic efficacy for wound healing. Rapamycin (Rapa), a potent immunosuppressant with favorable efficacy in organ transplantation, is limited by its systemic administration-associated toxicity and side effects. Therefore, addressing the short survival time of allogeneic skin grafts and minimizing the toxicity related to systemic application of immunosuppressive agents is an urgent requirement. Here, we present a topical formulation based on bioadhesive poly (lactic acid)-hyperbranched polyglycerol nanoparticles (BNPs) with surface-modified encapsulation of Rapamycin (Rapa/BNPs), applied for local immunosuppression in a murine model of allogeneic skin grafts. Our Rapa/BNPs significantly prolong nanoparticle retention, reduce infiltration of T lymphocytes and macrophages, decrease the level of pro-inflammatory cytokines and ultimately extend skin allograft survival with little systemic toxicity compared to free Rapa or Rapamycin-loaded non-bioadhesive nanoparticles (Rapa/NNPs) administration. In conclusion, Rapa/BNPs effectively deliver local immunosuppression and demonstrate potential for enhancing skin allograft survival while minimizing localized inflammation, thus potentially increasing patient survival rates for various types of skin defects.

Verteporfin-Loaded Bioadhesive Nanoparticles for the Prevention of Hypertrophic Scar.

Hypertrophic scarring (HS) is a common skin injury complication with unmet needs. Verteporfin (VP) should be an ideal HS-targeted therapeutic drug due to its efficient fibrosis and angiogenesis inhibitory abilities. However, its application is restricted by its side effects such as dose-dependent cytotoxicity on normal cells. Herein, the bioadhesive nanoparticles encapsulated VP (VP/BNPs) are successfully developed to attenuate the side effects of VP and enhance its HS inhibition effects by limiting VP releasing slowly and stably in the lesion site but not diffusing easily to normal tissues. VP/BNPs displayed significant inhibition on the proliferation, migration, collagen deposition, and vessel formation of human hypertrophic scar fibroblasts (HSFBs) and dermal vascular endothelial cells (HDVECs). In a rat tail HS model, VP/BNPs treated HS exhibits dramatic scar repression with almost no side effects compared with free VP or VP-loaded non-bioadhesive nanoparticles (VP/NNPs) administration. Further immunofluorescence analysis on scar tissue serial sections validated VP/BNPs effectively inhibited the collagen deposition and angiogenesis by firmly confined in the scar tissue and persistently releasing VP targeted to nucleus Yes-associated protein (nYAP) of HSFBs and HDVECs. These findings collectively suggest that VP/BNPs can be a promising and technically advantageous agent for HS therapies.

Pickering Emulsions and Viscoelastic Solutions Constructed by a Rosin-Based CO2-Responsive Surfactant.

Designing stimulus-switch viscoelastic solutions and Pickering emulsions with reversible CO2-responsive behavior remains a challenge. A rosin-based CO2-responsive surfactant, N-cetyl-maleimidepimaric acid N,N-dimethylenediamide (C16MPAN), was synthesized and used to prepare CO2-triggered viscoelastic solutions and Pickering emulsions. This surfactant exhibited excellent CO2-responsive performance in water and formed a viscoelastic solution. This viscoelastic system was investigated by dynamic light scattering (DLS), rheology, and cryogenic transmission electron microscopy (Cory-TEM). The shear viscosity of the system increased by 3-4 orders of magnitude after bubbling with CO2 and a large number of elongated, flexible, tubular wormlike micelles were observed. Further, Pickering emulsions were prepared by C16MPAN+ synergistically with cellulose nanocrystals (CNCs), whose stability and switchability were investigated via adsorption isotherm, droplet size, contact angle, and macroscopic photographs. C16MPAN+ was adsorbed with CNCs to form mechanical barriers at the oil-water interface, making the emulsion stable for at least three months, and desorption from CNCs enabled emulsion breaking. The cycle could be switched reversibly multiple times and the particle size distribution of emulsion was basically the same. This work enriches the application of biomass resources in intelligent responsive materials.

Quasi-pointwise two-step phase-shifting profilometry with the fringe parameters estimated statistically.

Fringe projection profilometry is a popularly used three-dimensional measurement technique in which phase-measuring algorithms based on two-step phase shifting are usually used because of their best tradeoff between measurement resolution and speed. Most two-step phase-shifting algorithms involve neighboring or other spatial operations, thus having degraded accuracies at edges and discontinuities of the measured object surface. Pointwise two-step algorithms enable overcoming this issue. With them, however, the offsets of the dynamic ranges of the projector and camera are usually improperly overlooked or inaccurately estimated, thus inducing errors in their measurement results. For solving this problem, this paper suggests a quasi-pointwise two-step phase-shifting algorithm for fringe projection profilometry. This algorithm models the captured fringe patterns practically by taking the offsets of the dynamic ranges of the projector and camera into account, and estimates the fringe parameters from the statistics of fringe intensities. As a result, we can calculate fringe phases in a pointwise way from two fringe patterns having a phase difference of π/2 radians. The simulation and experimental results demonstrate that the proposed method has a relatively low level of errors in measuring object surfaces having isolated regions and discontinuities.

Identification of molecular subgroups and establishment of risk model based on the response to oxidative stress to predict overall survival of patients with lung adenocarcinoma.

OBJECTIVE: Oxidative stress is associated with the occurrence and development of lung cancer. However, the specific association between lung cancer and oxidative stress is unclear. This study aimed to investigate the role of oxidative stress in the progression and prognosis of lung adenocarcinoma (LUAD). METHODS: The gene expression profiles and corresponding clinical information were collected from GEO and TCGA databases. Differentially expressed oxidative stress-related genes (OSRGs) were identified between normal and tumor samples. Consensus clustering was applied to identify oxidative stress-related molecular subgroups. Functional enrichment analysis, GSEA, and GSVA were performed to investigate the potential mechanisms. xCell was used to assess the immune status of the subgroups. A risk model was developed by the LASSO algorithm and validated using TCGA-LUAD, GSE13213, and GSE30219 datasets. RESULTS: A total of 40 differentially expressed OSRGs and two oxidative stress-associated subgroups were identified. Enrichment analysis revealed that cell cycle-, inflammation- and oxidative stress-related pathways varied significantly in the two subgroups. Furthermore, a risk model was developed and validated based on the OSRGs, and findings indicated that the risk model exhibits good prediction and diagnosis values for LUAD patients. CONCLUSION: The risk model based on the oxidative stress could act as an effective prognostic tool for LUAD patients. Our findings provided novel genetic biomarkers for prognosis prediction and personalized clinical treatment for LUAD patients.

Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial.

Human clinical trials are important tools to advance novel systemic therapies improve treatment outcomes for cancer patients. The few durable treatment options have led to a critical need to advance new therapeutics in hepatocellular carcinoma (HCC). Recent human clinical trials have shown that new combination immunotherapeutic regimens provide unprecedented clinical response in a subset of patients. Computational methods that can simulate tumors from mathematical equations describing cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico. To facilitate designing dosing regimen and identifying potential biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at tissue scale in HCC. This computational model is called a spatial quantitative systems pharmacology (spQSP) platform and it is also designed to simulate the effects of combination immunotherapy. We then validate the results from the spQSP system by leveraging real-world spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. The model output is compared with spatial data from Imaging Mass Cytometry (IMC). Both IMC data and simulation results suggest closer proximity between CD8 T cell and macrophages among non-responders while the reverse trend was observed for responders. The analyses also imply wider dispersion of immune cells and less scattered cancer cells in responders' samples. We also compared the model output with Visium spatial transcriptomics analyses of samples from post-treatment tumor resections in the original clinical trial. Both spatial transcriptomic data and simulation results identify the role of spatial patterns of tumor vasculature and TGFß in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial.

A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training.

Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. Endogenous BCIs based on electroencephalogram (EEG) signals, such as motor imagery (MI) BCIs, can provide some level of control. However, mastering spontaneous BCI control requires the users to generate discriminative and stable brain signal patterns by imagery, which is challenging and is usually achieved over a very long training time (weeks/months). Here, we propose a human-machine joint learning framework to boost the learning process in endogenous BCIs, by guiding the user to generate brain signals toward an optimal distribution estimated by the decoder, given the historical brain signals of the user. To this end, we first model the human-machine joint learning process in a uniform formulation. Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel "copy/new" feedback paradigm to help shape the signal generation of the subject toward the optimal distribution and 2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process. Specifically, the decoder reweighs the brain signals generated by the subject to focus more on "good" samples to cope with the learning process of the subject. Online and psuedo-online BCI experiments with 18 healthy subjects demonstrated the advantages of the proposed joint learning process over coadaptive approaches in both learning efficiency and effectiveness.

Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager.

Although immune checkpoint blockade therapies have shown evidence of clinical effectiveness in many types of cancer, the outcome of clinical trials shows that very few patients with colorectal cancer benefit from treatments with checkpoint inhibitors. Bispecific T cell engagers (TCEs) are gaining popularity because they can improve patients' immunological responses by promoting T cell activation. The possibility of combining TCEs with checkpoint inhibitors to increase tumor response and patient survival has been highlighted by preclinical and clinical outcomes. However, identifying predictive biomarkers and optimal dose regimens for individual patients to benefit from combination therapy remains one of the main challenges. In this article, we describe a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that includes specific processes of immune-cancer cell interactions and was created based on published data on colorectal cancer. We generated a virtual patient cohort with the model to conduct in silico virtual clinical trials for combination therapy of a PD-L1 checkpoint inhibitor (atezolizumab) and a bispecific T cell engager (cibisatamab). Using the model calibrated against the clinical trials, we conducted several virtual clinical trials to compare various doses and schedules of administration for two drugs with the goal of therapy optimization. Moreover, we quantified the score of drug synergy for these two drugs to further study the role of the combination therapy.