Treatment Patterns and Healthcare Resource Use in Medicare Beneficiaries with Parkinson's Disease.

Background: Studies on real-world treatment patterns and long-term economic burden of Parkinson's disease (PD) have been limited. Objective: To assess treatment patterns, healthcare resource utilization (HRU), and costs associated with PD symptoms and treatment-related adverse events (AEs) among Medicare beneficiaries in the United States. Methods: A 100% Medicare Fee-For-Service data (2006-2020) of patients with PD were analyzed. PD treatment patterns were described for the subset of patients who had no previously observed PD treatments or diagnoses (ie, the incident cohort). HRU and healthcare costs associated with PD symptoms were assessed for all patients with PD (ie, the overall cohort) and that associated with treatment-related AEs were assessed for the subset of patients who received PD treatments after PD diagnosis (ie, the active treatment cohort), using longitudinal models with repeated measures. Results: Overall, 318,582 patients were included (mean age at PD diagnosis: 77.4 years; 53.3% female). Among patients in the incident cohort (N=214,829), 51.1% initiated levodopa monotherapy and 5.9% initiated dopamine agonists (DAs) monotherapy as first-line treatment. The proportion of incident patients treated with DAs and other PD therapies generally increased from post-diagnosis years 1 to 10. The median time from diagnosis to PD treatment initiation was 2.0 months; the median time to treatment discontinuation was the longest with levodopa (18.7 months), followed by DAs (9.5 months). In the overall cohort, PD symptoms, especially motor symptoms and severe motor symptoms, were associated with significantly higher rates of HRU and costs. In the active treatment cohort (N=234,298), treatment-related AEs were associated with significantly higher rates of HRU and medical costs. Conclusion: While levodopa is still the mainstay of PD management, considerable heterogeneity exists in real-world treatment patterns. Overall, PD symptoms and AEs were associated with significantly higher HRU and healthcare costs, suggesting unmet medical needs for PD treatments with better tolerability profiles.

Defining subpopulations of differential drug response to reveal novel target populations.

Personalised medicine has predominantly focused on genetically altered cancer genes that stratify drug responses, but there is a need to objectively evaluate differential pharmacology patterns at a subpopulation level. Here, we introduce an approach based on unsupervised machine learning to compare the pharmacological response relationships between 327 pairs of cancer therapies. This approach integrated multiple measures of response to identify subpopulations that react differently to inhibitors of the same or different targets to understand mechanisms of resistance and pathway cross-talk. MEK, BRAF, and PI3K inhibitors were shown to be effective as combination therapies for particular BRAF mutant subpopulations. A systematic analysis of preclinical data for a failed phase III trial of selumetinib combined with docetaxel in lung cancer suggests potential indications in pancreatic and colorectal cancers with KRAS mutation. This data-informed study exemplifies a method for stratified medicine to identify novel cancer subpopulations, their genetic biomarkers, and effective drug combinations.

Opportunities for Data Science in the Pharmaceutical Industry: The Use of Data to Find Efficiencies in Drug Development Can?t Come Too Soon.

By the numbers, 2016 was not a good year for the U.S. pharmaceutical industry. As of early December, only 19 new drugs had been approved by the Food and Drug Administration (FDA), fewer than half of those approved in 2015 and the lowest number since 2007. Further, the FDA approved only 61% of submissions in 2016, compared to 95% in 2015 [1]. And, among the largest companies, the return on investment for research and development (R&D) fell to 3.7% [2].

In Vivo Brain Magnetic Resonance Spectroscopy: A Measurement of Biomarker Sensitivity to Post-Processing Algorithms.

Clinical translation of reported biomarkers requires reliable and consistent algorithms to derive biomarkers. However, the literature reports statistically significant differences between 1-D MRS measurements from control groups and subjects with disease states but frequently provides little information on the algorithms and parameters used to process the data. The sensitivity of in vivo brain magnetic resonance spectroscopy biomarkers is investigated with respect to parameter values for two key stages of post-acquisitional processing. Our effort is specifically motivated by the lack of consensus on approaches and parameter values for the two critical operations, water resonance removal, and baseline correction. The different stages of data processing also introduce varying levels of uncertainty and arbitrary selection of parameter values can significantly underutilize the intrinsic differences between two classes of signals. The sensitivity of biomarkers points to the need for a better understanding of how all stages of post-acquisitional processing affect biomarker discovery and ultimately, clinical translation. Our results also highlight the possibility of optimizing biomarker discovery by the careful selection of parameters that best reveal class differences. Using previously reported data and biomarkers, our results demonstrate that small changes in parameter values affect the statistical significance and corresponding effect size of biomarkers. Consequently, it is possible to increase the strength of biomarkers by selecting optimal parameter values in different spectral intervals. Our analyses with a previously reported data set demonstrate an increase in effect sizes for wavelet-based biomarkers of up to 36%, with increases in classification performance of up to 12%.

Algorithms for characterizing brain metabolites in two-dimensional in vivo MR correlation spectroscopy.

Traditional analyses of in vivo 1D MR spectroscopy of brain metabolites have been limited to the inspection of one-dimensional free induction decay (FID) signals from which only a limited number of metabolites are clearly observable. In this article we introduce a novel set of algorithms to process and characterize two-dimensional in vivo MR correlation spectroscopy (2D COSY) signals. 2D COSY data was collected from phantom solutions of topical metabolites found in the brain, namely glutamine, glutamate, and creatine. A statistical peak-detection and object segmentation algorithm is adapted for 2D COSY signals and applied to phantom solutions containing varied concentrations of glutamine and glutamate. Additionally, quantitative features are derived from peak and object structures, and we show that these measures are correlated with known phantom metabolite concentrations. These results are encouraging for future studies focusing on neurological disorders that induce subtle changes in brain metabolite concentrations and for which accurate quantitation is important.

Neuro magnetic resonance spectroscopy using wavelet decomposition and statistical testing identifies biochemical changes in people with spinal cord injury and pain.

Spinal cord injury (SCI) can be accompanied by chronic pain, the mechanisms for which are poorly understood. Here we report that magnetic resonance spectroscopy measurements from the brain, collected at 3T, and processed using wavelet-based feature extraction and classification algorithms, can identify biochemical changes that distinguish control subjects from subjects with SCI as well as subdividing the SCI group into those with and without chronic pain. The results from control subjects (n=10) were compared to those with SCI (n=10). The SCI cohort was made up of subjects with chronic neuropathic pain (n=5) and those without chronic pain (n=5). The wavelet-based decomposition of frequency domain MRS signals employs statistical significance testing to identify features best suited to discriminate different classes. Moreover, the features benefit from careful attention to the post-processing of the spectroscopy data prior to the comparison of the three cohorts. The spectroscopy data, from the thalamus, best distinguished control subjects without SCI from those with SCI with a sensitivity and specificity of 0.9 (Percentage of Correct Classification). The spectroscopy data obtained from the prefrontal cortex and anterior cingulate cortex both distinguished between SCI subjects with chronic neuropathic pain and those without pain with a sensitivity and specificity of 1.0. In this study, where two underlying mechanisms co-exist (i.e. SCI and pain), the thalamic changes appear to be linked more strongly to SCI, while the anterior cingulate cortex and prefrontal cortex changes appear to be specifically linked to the presence of pain.

Joint analysis of differential mobility spectrometer and mass spectrometer features for tuberculosis biomarkers.

In this article, we present results of recent efforts to identify biomarkers for tuberculosis using a differential mobility spectrometer (DMS). We focus specifically on the capability of exploiting a data collection system that employs a DMS in parallel with a mass spectrometer. This system permits previously developed algorithms for DMS to be used in conjunction with a device considered a gold-standard for chemical identification, making it a unique discovery tool for the determination of biomarkers.