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Around half of adolescent pregnancies in low- and middle-income countries are unintended, contributing to millions of unsafe abortions per year. Adolescents 360 (A360), a girl-centred initiative, aimed to increase voluntary uptake of modern contraceptives among adolescents in Nigeria, Ethiopia and Tanzania. We evaluated the effectiveness and cost-effectiveness of A360 in increasing modern contraceptive use in selected geographies. We used before-and-after cross-sectional studies of adolescent girls in four settings. Two Nigerian settings had purposefully selected comparison areas. Baseline and endline household surveys were conducted. The primary study outcome was modern contraceptive prevalence rate (mCPR). Secondary outcomes mapped onto the A360 Theory of Change. Interpretation was aided by a process evaluation along with secular mCPR trends and self-reported A360 exposure data. Incremental design and implementation costs were calculated from implementer systems, site visits, surveys, and interviews. mCPR change was modelled into maternal disability-adjusted life years (DALY) averted to calculate incremental cost-effectiveness ratios. In Oromia, Ethiopia, mCPR increased by 5% points (95% CI 1-10; n = 1,697). In Nigeria, there was no evidence of an effect of A360 on mCPR in Nasarawa (risk ratio: 0·96, 95% CI: 0·76-1·21; n = 5,414) or in Ogun (risk ratio: 1·08, 95% CI: 0·92-1·26; n = 3,230). In Mwanza, Tanzania, mCPR decreased by 9% points (-17 to -0.3; n = 1,973). Incremental cost per DALY averted were $30,855 in Oromia, $111,416 in Nasarawa, $30,114 in Ogun, and $25,579 in Mwanza. Costs per DALY averted were 14-53 times gross domestic product per capita. A360 did not lead to increased adolescent use of modern contraceptives at a population level, except in Oromia, and was not cost-effective. This novel adolescent-centred design approach showed some promise in addressing the reproductive health needs of adolescents, but must be accompanied by efforts to address the contextual drivers of low modern contraceptive use.
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Background: Relapsed glioblastoma (GBM) is often an imminently fatal condition with limited therapeutic options. Computation biological modeling, i.e., biosimulation, of comprehensive genomic information affords the opportunity to create a disease avatar that can be interrogated in silico with various drug combinations to identify the most effective therapies. Case Description: We report the outcome of a GBM patient with chromosome 12q amplification who achieved substantial disease remission from a novel therapy using this approach. Following next generation sequencing (NGS) was performed on the tumor specimen. Mutation and copy number changes were input into a computational biologic model to create an avatar of disease behavior and the malignant phenotype. In silico responses to various drug combinations were biosimulated in the disease network. Efficacy scores representing the computational effect of treatment for each strategy were generated and compared to each other to ascertain the differential benefit in drug response from various regimens. Biosimulation identified CDK4/6 inhibitors, nelfinavir and leflunomide to be effective agents singly and in combination. Upon receiving this treatment, the patient achieved a prompt and clinically meaningful remission lasting 6 months. Conclusions: Biosimulation has utility to identify active treatment combinations, stratify treatment options and identify investigational agents relevant to patients' comprehensive genomic abnormalities. Additionally, the combination of abemaciclib and nelfinavir appear promising for GBM and potentially other cancers harboring chromosome 12q amplification.
RESUMO
BACKGROUND: A randomized trial in glioblastoma patients with methylated-MGMT (m-MGMT) found an improvement in median survival of 16.7 months for combination therapy with temozolomide (TMZ) and lomustine, however the approach remains controversial and relatively under-utilized. Therefore, we sought to determine whether comprehensive genomic analysis can predict which patients would derive large, intermediate, or negligible benefits from the combination compared to single agent chemotherapy. METHODS: Comprehensive genomic information from 274 newly diagnosed patients with methylated-MGMT glioblastoma (GBM) was downloaded from TCGA. Mutation and copy number changes were input into a computational biologic model to create an avatar of disease behavior and the malignant phenotypes representing hallmark behavior of cancers. In silico responses to TMZ, lomustine, and combination treatment were biosimulated. Efficacy scores representing the effect of treatment for each treatment strategy were generated and compared to each other to ascertain the differential benefit in drug response. RESULTS: Differential benefits for each drug were identified, including strong, modest-intermediate, negligible, and deleterious (harmful) effects for subgroups of patients. Similarly, the benefits of combination therapy ranged from synergy, little or negligible benefit, and deleterious effects compared to single agent approaches. CONCLUSIONS: The benefit of combination chemotherapy is predicted to vary widely in the population. Biosimulation appears to be a useful tool to address the disease heterogeneity, drug response, and the relevance of particular clinical trials observations to individual patients. Biosimulation has potential to spare some patients the experience of over-treatment while identifying patients uniquely situated to benefit from combination treatment. Validation of this new artificial intelligence tool is needed.
