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Accurate prediction of synergistic effects of drug combinations can reduce the experimental costs for drug development and facilitate the discovery of novel efficacious combination therapies for clinical studies. The drug combinations with high synergy scores are regarded as synergistic ones, while those with moderate or low synergy scores are additive or antagonistic ones. The existing methods usually exploit the synergy data from the aspect of synergistic drug combinations, paying little attention to the additive or antagonistic ones. Also, they usually do not leverage the common patterns of drug combinations across different cell lines. In this paper, we propose a multi-channel graph autoencoder (MGAE)-based method for predicting the synergistic effects of drug combinations (DC), and shortly denote it as MGAE-DC. A MGAE model is built to learn the drug embeddings by considering not only synergistic combinations but also additive and antagonistic ones as three input channels. The later two channels guide the model to explicitly characterize the features of non-synergistic combinations through an encoder-decoder learning process, and thus the drug embeddings become more discriminative between synergistic and non-synergistic combinations. In addition, an attention mechanism is incorporated to fuse each cell-line's drug embeddings across various cell lines, and a common drug embedding is extracted to capture the invariant patterns by developing a set of cell-line shared decoders. The generalization performance of our model is further improved with the invariant patterns. With the cell-line specific and common drug embeddings, our method is extended to predict the synergy scores of drug combinations by a neural network module. Experiments on four benchmark datasets demonstrate that MGAE-DC consistently outperforms the state-of-the-art methods. In-depth literature survey is conducted to find that many drug combinations predicted by MGAE-DC are supported by previous experimental studies. The source code and data are available at https://github.com/yushenshashen/MGAE-DC. Author summary: Drug combination therapy is widely employed for various complex diseases because of its advantages of enhancing the efficiency, overcoming the drug resistance and reducing dose-dependent toxicity relative to monotherapy. To identify novel reliable and efficacious drug combinations for the patients, various methods have been proposed in the past decades. The trial-based methods are based on the clinical trials directly, but may cause patients to receive unnecessary or even harmful treatments. The experimental methods are labour and cost intensive, it is infeasible to test the complete drug combination space due to the combinatorial explosion. Computational methods provide attractive solutions to make the prediction based on the known data in silico, and thus narrow down the searching range of potential combinations. We have developed a novel deep learning method, named MGAE-DC, for predicting the synergistic effects of drug combinations. Our method explicitly characterizes not only synergistic combinations but also non-synergistic ones to obtain discriminative drug embeddings. Our method also learns common features of drug combinations across the cell lines for improved generalization performance. Computational experiments have verified the effectiveness of MGAE-DC. [ABSTRACT FROM AUTHOR]

Background: Malaria remains one of the leading causes of morbidity, and mortality in Uganda. A large proportion of malaria symptomatic patients seek healthcare in private sector. However, availability and affordability are major barriers to access to effective treatment. The private sector copayment mechanism in Uganda aims to increase availability and affordability of antimalarial agents. Our study assessed availability, price, and market share of quality assured artemisinin-based combination therapies (QAACTs) in private drug outlets in selected districts during the implementation of copayment mechanism. Methods: This was a cross-sectional survey of anti-malarial agents in private drug outlets in in selected moderate-to-high (Tororo, and Apac districts) and low (Kabale and Mbarara districts) malaria transmission settings. Following the World Health Organization/Health Action International (WHO/HAI) criteria, an audit of the antimalarial agents was done using a checklist to determine availability, price, and market share of QAACTs. Data were entered in Epi-data and analyzed in STATA ver 14.0 at 95% confidence level. Results: A total of twenty-eight (28) private drug outlets (pharmacies and drug shops) were included in the survey. One in seven (20/144: 95%CI: 9.1, 20.6) of the antimalarial agents in private drug outlets were quality assured artemisinin-based combination therapies (QAACT). Artemether-lumefantrine (AL), 8.9% (11/124) and Artesunate-Amodiaquine (AQ), 7.3% (9/124) were the only QAACTs present in the drug outlets at the time of the survey. The majority, 86.1%% (124/144) of antimalarial agents present in stock in the drug outlets were artemisinin based. The most common, 38.9% (56/144) ACT in the drug outlets was Dihydroartemisinin-Piperaquine (DHP). Most, 69.4% (100/144) of the antimalarial agents were in high malaria transmission settings. The cost of ACT antimalarial agents is high in the country, USD 1.4 (Artemether-Lumefantrine, AL), USD 2.4 (Dihydroartemisinin-Piperaquine, DP), the first line and second-line agents respectively for treatment of uncomplicated malaria in Uganda. There was a statistically significant difference between the dispensing price of 'Green leaf' ACTs (QAACT) and the recommended price (p<0.001). Predictors of availability of QAACT in private drug outlets include pharmacy drug outlet (aPR:0.4; 95%CI: 0.2, 0.9) and dispensing price more than 3000UGX (USD 0.83) (aPR: 0.4, 95%CI: 0.1, 0.51). Conclusion: Quality assured artemisinin-based combination therapies (QAACTs) are not common in private drug outlets in selected districts in Uganda. All the drug outlets had at least one ACT antimalarial agent present on the day of the survey. The dispensing price of QAACTs was significantly higher than the recommended markup price. There is need for awareness creation, surveillance, and monitoring of the implementation of Copayment mechanism in the country. [ABSTRACT FROM AUTHOR]

Drug combination therapies can improve drug efficacy, reduce drug dosage, and overcome drug resistance in cancer treatments. Current research strategies to determine which drug combinations have a synergistic effect rely mainly on clinical or empirical experience and screening predefined pools of drugs. Given the number of possible drug combinations, the speed, and scope to find new drug combinations are very limited using these methods. Due to the exponential growth in the number of drug combinations, it is difficult to test all possible combinations in the lab. There are several large-scale public genomic and phenotypic resources that provide data from single drug-treated cells as well as data from small molecule treated cells. These databases provide a wealth of information regarding cellular responses to drugs and offer an opportunity to overcome the limitations of the current methods. Developing a new advanced data processing and analysis strategy is imperative and a computational prediction algorithm is highly desirable. In this paper, we developed a computational algorithm for the enrichment of synergistic drug combinations using gene regulatory network knowledge and an operational module unit (OMU) system which we generate from single drug genomic and phenotypic data. As a proof of principle, we applied the pipeline to a group of anticancer drugs and demonstrate how the algorithm could help researchers efficiently find possible synergistic drug combinations using single drug data to evaluate all possible drug pairs. [ABSTRACT FROM AUTHOR]

Published studies have generated compelling results indicating that type I IFN modulates function of HSV-1 latency-associated transcript (LAT). One member of type I IFN is IFNα2A also called Roferon-A). IFNα2A has been used in monotherapy or in combination therapy with other drugs to treat viral infections and different kinds of cancer in humans. The goal of this study was to determine whether the absence of IFNα2A affects primary and latent infections in ocularly infected mice. Therefore, we generated a mouse strain lacking IFNα2A expression (IFNα2A-/-). Ocular HSV-1 replication, IFN and immune cell expressions on days 3 and 5 post infection (PI), as well as eye disease, survival, latency-reactivation, and T cell exhaustion were evaluated in ocularly infected IFNα2A-/- and wild type (WT) control mice. Absence of IFNα2A did not affect other members of the IFNα family but it affected IFNβ and IFNγ expressions as well as some immune cells on day 5 PI compared to WT mice. Viral replication in the eye, eye disease, and survival amongst ocularly infected IFNα2A-/- mice were similar to that of WT infected mice. The absence of IFNα2A significantly reduced the levels of latency and T cell exhaustion but not time of reactivation compared with control mice. Our results suggest that blocking IFNα2A expression may be a useful tool in reducing latency and the subsequent side effects associated with higher levels of latency. Author summary: Many viruses have evolved strategies to circumvent the IFN system, including (i) inhibition of IFN production; (ii) blocking of downstream signaling events that occur after cytokine-receptor binding; and (iii) inhibition of the functions of proteins, which are induced by IFN, including apoptotic proteins. The interferons (IFNs) are of considerable interest in the control of HSV-1 infection due to their rapid generation in response to viral infection and their antiviral activities. Type I interferons, which includes interferon alpha (IFNα), are a family of cytokines important for control of viral infections. There are at least 14 IFNα genes in mouse, amongst which includes the gene encoding IFNα2A that play an important role in human studies. The mechanisms by which HSV-1 interferes with type I IFN responses during primary infection, establishment of latency and reactivation are not fully understood. Thus, to evaluate the importance of IFNα2A on HSV-1 infectivity, we generated IFNα2A knockout mice. The absence of IFNα2A significantly reduced latency and T cell exhaustion, but not virus reactivation. [ABSTRACT FROM AUTHOR]

Background: Ginseng-containing traditional medicine preparations (G-TMPs) in combination with fluoropyrimidine-based chemotherapy (FBC) are well-known treatments for advanced gastric cancer (AGC), with a superior efficacy to FBC alone. However, evidence regarding their efficacy remains limited. The purpose of this meta-analysis is to evaluate the efficacy and safety of G-TMPs in combination with FBC for the treatment of AGC. Methods: Eight electronic databases were searched for randomized controlled trials (RCTs) using G-TMPs with FBC for the treatment of AGC. The primary outcome included the tumor response, while the secondary outcomes included the quality of life (QoL), proportions of peripheral blood lymphocytes, adverse drug reactions (ADRs), and levels of cancer biomarkers. The quality of evidence for each outcome was assessed using GRADE profilers. Results: A total of 1,960 participants were involved in the 26 RCTs included. Patients treated with FBC plus G-TMPs had better objective response (risk ratio [RR] = 1.23, 95% confidence interval [CI]: 1.13 to 1.35, p < 0.00001) and disease control (RR = 1.13, 95% CI: 1.08 to 1.19, p < 0.00001) rates than those treated with FBC alone. Additionally, the combination group had a better QoL, higher proportions of CD3+ T cells, CD4+ T cells, and natural killer cells, as well as a higher CD4+/CD8+ T-cell ratio. Furthermore, lower levels of CA19-9, CA72-4, and CEA were confirmed in the combination treatment group. In addition, G-TMPs reduced the incidence of ADRs during chemotherapy. Conclusion: In combination with FBC, G-TMPs can potentially enhance efficacy, reduce ADRs, and improve prognosis for patients with AGC. However, high-quality randomized studies remain warranted. Systematic review registration: PROSPERO Number: CRD42021264938. [ABSTRACT FROM AUTHOR]

Purpose: To assess the efficacy of combination chemotherapy with nafamostat mesilate, gemcitabine and S-1 for unresectable pancreatic cancer patients. Materials and methods: The study was conducted as a single-arm, single center, institutional review board-approved phase II trial. Patients received nafamosntat mesilate (4.8 mg/kg continuous transregional arterial infusion) with gemcitabine (1000 mg/m2 transvenous) on days 1 and15, and with oral S-1 [(80 mg/day (BSA<1.25 m2), 100 mg/day (1.25 ≤ BSA<1.5 m2), or 120 mg/day (BSA ≥1.5 m2)] on days 1–14 or, days 1–7 and 15–21. This regimen was repeated at 28-day intervals. Results: Forty-seven evaluable patients (Male/Female: 31/16, Age (median): 66 (range 35–78) yrs, Stage III/IV 10/37.) were candidates in this study. Two patients in stage III (20%) could undergo conversion surgery. Twenty-four patients (51%) underwent subsequent treatment (1st line/ 2nd line / 4th line, 13/ 10/ 1, FOLFIRINOX: 12, GEM/nab-PTX: 18, TAS-118: 3, chemoradiation with S-1: 2, GEM/Erlotinib: 1, nal-IRI: 1, surgery: 2). Median PFS and OS were 9.7 (95% CI, 8.9–14.7 mo) and 14.2 months (99% CI, 13.3–23.9 mo), respectively. Median PFS in stage IV patients was 9.2 months (95% CI, 8.4–12.0 mo). Median OS in patients without subsequent treatment was 10.8 months (95% CI, 9.1–13.8 mo). Median OS in patients with subsequent treatment was 19.3 months (95% CI, 18.9–31.9 mo). Grade 4 treatment-related hematological toxicities were encountered in 7 patients. Two patients developed grade 3 allergic reaction after 6 cycles or later. No febrile neutropenia has been observed. Conclusion: NAM/GEM/S-1 therapy is safe and could be promising option for unresectable pancreatic cancer, especially for stage IV cancer. [ABSTRACT FROM AUTHOR]

Background: Scaling up shorter regimens for tuberculosis (TB) prevention such as once weekly isoniazid-rifapentine (3HP) taken for 3 months is a key priority for achieving targets set forth in the World Health Organization's (WHO) END TB Strategy. However, there are few data on 3HP patient acceptance and completion in the context of routine HIV care in sub-Saharan Africa.Methods and Findings: The 3HP Options Trial is a pragmatic, parallel type 3 effectiveness-implementation randomized trial comparing 3 optimized strategies for delivering 3HP-facilitated directly observed therapy (DOT), facilitated self-administered therapy (SAT), or informed choice between DOT and SAT using a shared decision-making aid-to people receiving care at a large urban HIV clinic in Kampala, Uganda. Participants and healthcare providers were not blinded to arm assignment due to the nature of the 3HP delivery strategies. We conducted an interim analysis of participants who were enrolled and exited the 3HP treatment period between July 13, 2020 and April 30, 2021. The primary outcome, which was aggregated across trial arms for this interim analysis, was the proportion who accepted and completed 3HP (≥11 of 12 doses within 16 weeks of randomization). We used Bayesian inference analysis to estimate the posterior probability that this proportion would exceed 80% under at least 1 of the 3HP delivery strategies, a coprimary hypothesis of the trial. Through April 2021, 684 participants have been enrolled, and 479 (70%) have exited the treatment period. Of these 479 participants, 309 (65%) were women, mean age was 41.9 years (standard deviation (SD): 9.2), and mean time on antiretroviral therapy (ART) was 7.8 years (SD: 4.3). In total, 445 of them (92.9%, 95% confidence interval (CI): [90.2 to 94.9]) accepted and completed 3HP treatment. There were no differences in treatment acceptance and completion by sex, age, or time on ART. Treatment was discontinued due to a documented adverse event (AE) in 8 (1.7%) patients. The probability that treatment acceptance and completion exceeds 80% under at least 1 of the three 3HP delivery strategies was greater than 99%. The main limitations are that the trial was conducted at a single site, and the interim analysis focused on aggregate outcome data to maintain blinding of investigators to arm-specific outcomes.Conclusions: 3HP was widely accepted by people living with HIV (PLHIV) in Uganda, and very high levels of treatment completion were achieved in a programmatic setting. These findings show that 3HP can enable effective scale-up of tuberculosis preventive therapy (TPT) in high-burden countries, particularly when delivery strategies are tailored to target known barriers to treatment completion.Trial Registration: ClinicalTrials.gov NCT03934931. [ABSTRACT FROM AUTHOR]