ScaffComb: A Phenotype-Based Framework for Drug Combination Virtual Screening in Large-Scale Chemical Datasets.

Combinational therapy is used for a long time in cancer treatment to overcome drug resistance related to monotherapy. Increased pharmacological data and the rapid development of deep learning methods have enabled the construction of models to predict and screen drug pairs. However, the size of drug libraries is restricted to hundreds to thousands of compounds. The ScaffComb framework, which aims to bridge the gaps in the virtual screening of drug combinations in large-scale databases, is proposed here. Inspired by phenotype-based drug design, ScaffComb integrates phenotypic information into molecular scaffolds, which can be used to screen the drug library and identify potent drug combinations. First, ScaffComb is validated using the US food and drug administration dataset and known drug combinations are successfully reidentified. Then, ScaffComb is applied to screen the ZINC and ChEMBL databases, which yield novel drug combinations and reveal an ability to discover new synergistic mechanisms. To our knowledge, ScaffComb is the first method to use phenotype-based virtual screening of drug combinations in large-scale chemical datasets.

Diabetes Healthcare Professionals Use Multiple Continuous Glucose Monitoring Data Indicators to Assess Glucose Management.

BACKGROUND: Continuous glucose monitoring (CGM) offers multiple data features that can be leveraged to assess glucose management. However, how diabetes healthcare professionals (HCPs) actually assess CGM data and the extent to which they agree in assessing glycemic management are not well understood. METHODS: We asked HCPs to assess ten de-identified CGM datasets (each spanning seven days) and rank order each day by relative glycemic management (from "best" to "worst"). We also asked HCPs to endorse features of CGM data that were important in making such assessments. RESULTS: In the study, 57 HCPs (29 endocrinologists; 28 diabetes educators) participated. Hypoglycemia and glycemic variance were endorsed by nearly all HCPs to be important (91% and 88%, respectively). Time in range and daily lows and highs were endorsed more frequently by educators (all Ps < .05). On average, HCPs endorsed 3.7 of eight data features. Overall, HCPs demonstrated agreement in ranking days by relative glycemic control (Kendall's W = .52, P < .001). Rankings were similar between endocrinologists and educators (R2 = .90, Cohen's kappa = .95, mean absolute error = .4 [all Ps < .05]; Mann-Whitney U = 41, P = .53). CONCLUSIONS: Consensus in the endorsement of certain data features and agreement in assessing glycemic management were observed. While some practice-specific differences in feature endorsement were found, no differences between educators and endocrinologists were observed in assessing glycemic management. Overall, HCPs tended to consider CGM data holistically, in alignment with published recommendations, and made converging assessments regardless of practice.

[Lack of medical or dental care: Impact on health expenditures for people with chronic disease]. / L'effet du non-recours aux soins médicaux et dentaires sur les dépenses de santé des personnes ayant une maladie chronique.

BACKGROUND: People with chronic disease often have dental (especially periodontal) disorders. Nevertheless, people with chronic disease seek dental care less often than others. We wanted to know if there is a relationship between the consumption of medical care and the consumption of dental care, and if so if the relationship is especially strong for people with chronic disease. METHODS: We conducted a longitudinal study that combined two data-sets: consumption data from the French National Health Insurance Fund and health and socioeconomic welfare data collected with a dedicated national survey. We studied healthcare expenditure and analyzed the association between healthcare consumption, health status and healthcare expenditure over a four-year period (2010-2013). RESULTS: People who did not seek medical or dental care in 2010 exhibited irregular consumer behavior thereafter. This pattern was particularly evident among those with chronic disease whose healthcare expenditures did not stabilize during the study period compared with the rest of the study population. Among people who did not seek medical care in 2010, variation in average dental care expenditure was 91% in people with chronic disease versus 42% for those without chronic disease. Lack of medical care during the first year of the study was also associated with greater expenditure-delay in people with chronic disease (77%) compared with 15% in people without chronic disease. CONCLUSION: The lack of medical or dental care in 2010 for people with chronic disease did not lead to an increase in medical and dental consumption in the following years. The catch-up delay was longer than four years. This highlights a problem of monitoring and identifies a marginalized population within the healthcare system.

How health leaders can benefit from predictive analytics.

Predictive analytics can support a better integrated health system providing continuous, coordinated, and comprehensive person-centred care to those who could benefit most. In addition to dollars saved, using a predictive model in healthcare can generate opportunities for meaningful improvements in efficiency, productivity, costs, and better population health with targeted interventions toward patients at risk.

The customer is always right? Subjective target symptoms and treatment preferences in patients with psychosis.

Clinicians and patients differ concerning the goals of treatment. Eighty individuals with schizophrenia were assessed online about which symptoms they consider the most important for treatment, as well as their experience with different interventions. Treatment of affective and neuropsychological problems was judged as more important than treatment of positive symptoms (p < 0.005). While most individuals had experience with Occupational and Sports Therapy, only a minority had received Cognitive-Behavioral Therapy, Family Therapy, and Psychoeducation with family members before. Patients appraised Talk, Psychoanalytic, and Art Therapy as well as Metacognitive Training as the most helpful treatments. Clinicians should carefully take into consideration patients' preferences, as neglect of consumers' views may compromise outcome and adherence to treatment.

A 16 Yin Yang gene expression ratio signature for ER+/node- breast cancer.

Breast cancer is one of the leading causes of cancer death in women. It is a complex and heterogeneous disease with different clinical outcomes. Stratifying patients into subgroups with different outcomes could help guide clinical decision making. In this study, we used two opposing groups of genes, Yin and Yang, to develop a prognostic expression ratio signature. Using the METABRIC cohort we identified a16-gene signature capable of stratifying breast cancer patients into four risk levels with intention that low-risk patients would not undergo adjuvant systemic therapy, intermediate-low-risk patients will be treated with hormonal therapy only, and intermediate-high- and high-risk groups will be treated by chemotherapy in addition to the hormonal therapy. The 16-gene signature for four risk level stratifications of breast cancer patients has been validated using 14 independent datasets. Notably, the low-risk group (n = 51) of 205 estrogen receptor-positive and node negative (ER+/node-) patients from three different datasets who had not had any systemic adjuvant therapy had 100% 15-year disease-specific survival rate. The Concordance Index of YMR for ER+/node negative patients is close to the commercially available signatures. However, YMR showed more significance (HR = 3.7, p = 8.7e-12) in stratifying ER+/node- subgroup than OncotypeDx (HR = 2.7, p = 1.3e-7), MammaPrint (HR = 2.5, p = 5.8e-7), rorS (HR = 2.4, p = 1.4e-6), and NPI (HR = 2.6, p = 1.2e-6). YMR signature may be developed as a clinical tool to select a subgroup of low-risk ER+/node- patients who do not require any adjuvant hormonal therapy (AHT).

Quantification of diabetes comorbidity risks across life using nation-wide big claims data.

Despite substantial progress in the study of diabetes, important questions remain about its comorbidities and clinical heterogeneity. To explore these issues, we develop a framework allowing for the first time to quantify nation-wide risks and their age- and sex-dependence for each diabetic comorbidity, and whether the association may be consequential or causal, in a sample of almost two million patients. This study is equivalent to nearly 40,000 single clinical measurements. We confirm the highly controversial relation of increased risk for Parkinson's disease in diabetics, using a 10 times larger cohort than previous studies on this relation. Detection of type 1 diabetes leads detection of depressions, whereas there is a strong comorbidity relation between type 2 diabetes and schizophrenia, suggesting similar pathogenic or medication-related mechanisms. We find significant sex differences in the progression of, for instance, sleep disorders and congestive heart failure in diabetic patients. Hypertension is a highly sex-sensitive comorbidity with females being at lower risk during fertile age, but at higher risk otherwise. These results may be useful to improve screening practices in the general population. Clinical management of diabetes must address age- and sex-dependence of multiple comorbid conditions.

Cost-effectiveness and cost-utility of selective serotonin reuptake inhibitors, serotonin norepinephrine reuptake inhibitors, and tricyclic antidepressants in depression with comorbid cardiovascular disease.

OBJECTIVE: There is a lack of clarity in the literature regarding the cost-effectiveness and cost-utility of antidepressants for treating real-world patients. The impact of comorbid cardiovascular disease (CVD) on the economic evaluations of antidepressants remains to be determined. METHOD: Adult patients prescribed with antidepressants for depression were identified from the National Health Insurance Research Database in Taiwan. A cost-effectiveness and cost-utility analysis was conducted comparing selective serotonin reuptake inhibitors (SSRIs), serotonin norepinephrine reuptake inhibitors (SNRIs), and tricyclic antidepressants (TCAs), and by the presence of comorbid CVD. RESULTS: In terms of treatment success rates, SSRIs were the most cost-effective option compared to TCAs and SNRIs as revealed in the incremental cost-effectiveness ratios. The cost-effectiveness acceptability curves further showed differential findings in the cost-utility results by the presence of comorbid CVD. CONCLUSION: To improve treatment success rates and quality-adjusted life years, SSRIs can be considered the most cost-effective option. Future research is needed to further clarify the impacts of physical comorbidities and other associated factors on the cost-effectiveness and cost-utility of pharmacological treatments in patients with depression.