Disease progression strikingly differs in research and real-world Parkinson's populations.

Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data, and advances in natural language processing, particularly large language models, allow for a more granular comparison of populations than previously possible. This study includes two research populations and two real-world data-derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using a manually validated natural language processing with a large language model to extract measurements of PD progression. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p < 0.001; mini-mental state exam median decline 0.28 vs. 0.11, p < 0.001; and clinically recognized cognitive decline, p = 0.001). In real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p < 0.001). After diagnosis, in real-world cohorts, treatment with PD medications has initiated an average of 2.3 years later (95% CI: [2.1-2.4]; p < 0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real-world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.

Disease progression strikingly differs in research and real-world Parkinson's populations.

Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data and recent advances in natural language processing, particularly large language models, allow for a more granular comparison of populations and the methods of data collection describing these populations than previously possible. This study includes two research populations and two real-world data derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using natural language processing with a large language model to extract measurements of PD progression. This extraction process is manually validated for accuracy. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p<0.001; mini-mental state exam median decline 0.28 vs. 0.11, p<0.001; and clinically recognized cognitive decline, p=0.001). In the real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p<0.001). After diagnosis, in real-world cohorts, treatment with PD medications is initiated 2.3 years later on average (95% CI: [2.1-2.4]; p<0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging using existing data. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.

An Overview of Current Methods for Real-world Applications to Generalize or Transport Clinical Trial Findings to Target Populations of Interest.

It has been well established that randomized clinical trials have poor external validity, resulting in findings that may not apply to relevant-or target-populations. When the trial is sampled from the target population, generalizability methods have been proposed to address the applicability of trial findings to target populations. When the trial sample and target populations are distinct, transportability methods may be applied for this purpose. However, generalizability and transportability studies present challenges, particularly around the strength of their conclusions. We review and summarize state-of-the-art methods for translating trial findings to target populations. We additionally provide a novel step-by-step guide to address these challenges, illustrating principles through a published case study. When conducted with rigor, generalizability and transportability studies can play an integral role in regulatory decisions by providing key real-world evidence.

Calibration and Uncertainty in Neural Time-to-Event Modeling.

Models for predicting the time of a future event are crucial for risk assessment, across a diverse range of applications. Existing time-to-event (survival) models have focused primarily on preserving pairwise ordering of estimated event times (i.e., relative risk). We propose neural time-to-event models that account for calibration and uncertainty while predicting accurate absolute event times. Specifically, an adversarial nonparametric model is introduced for estimating matched time-to-event distributions for probabilistically concentrated and accurate predictions. We also consider replacing the discriminator of the adversarial nonparametric model with a survival-function matching estimator that accounts for model calibration. The proposed estimator can be used as a means of estimating and comparing conditional survival distributions while accounting for the predictive uncertainty of probabilistic models. Extensive experiments show that the distribution matching methods outperform existing approaches in terms of both calibration and concentration of time-to-event distributions.

Transporting observational study results to a target population of interest using inverse odds of participation weighting.

Inverse odds of participation weighting (IOPW) has been proposed to transport clinical trial findings to target populations of interest when the distribution of treatment effect modifiers differs between trial and target populations. We set out to apply IOPW to transport results from an observational study to a target population of interest. We demonstrated the feasibility of this idea with a real-world example using a nationwide electronic health record derived de-identified database from Flatiron Health. First, we conducted an observational study that carefully adjusted for confounding to estimate the treatment effect of fulvestrant plus palbociclib relative to letrozole plus palbociclib as a second-line therapy among estrogen receptor (ER)-positive, human epidermal growth factor receptor (HER2)-negative metastatic breast cancer patients. Second, we transported these findings to the broader cohort of patients who were eligible for a first-line therapy. The interpretation of the findings and validity of such studies, however, rely on the extent that causal inference assumptions are met.

Characterizing real world safety profile of oral Janus kinase inhibitors among adult atopic dermatitis patients: evidence transporting from the rheumatoid arthritis population.

OBJECTIVE: To address potential safety concerns of Janus Kinase Inhibitors (JAK-Is), we characterized their safety profile in the atopic dermatitis (AD) patient population. METHODS: In this retrospective observational study, we used propensity score-based methods and a Poisson modeling framework to estimate the incidence of health outcomes of interest (HOI) for the AD patient. To that end, two mutually exclusive cohorts were created using a real world data resource: a rheumatoid arthritis (RA) cohort, where we directly quantify the safety risk of JAK-Is on HOIs, and an AD cohort, that comprises the target population of interest and to whom we transport the results obtained from the RA cohort. The RA cohort included all adults who filled at least one prescription for a JAK-I (tofacitinib, baricitinib, or upadacitinib) between 1 January 2017 and 31 January 2020. The AD cohort consisted of all adults diagnosed with AD during the same period. We first estimated the incidence rate of each HOI in the RA cohort, and then transported the results to the AD population. RESULTS: The RA and AD cohorts included 5,296 and 261,855 patients, respectively. On average, patients in the AD cohort were younger, more often male, more likely to be Asian, and had higher household income. They also had a lower prevalence of several comorbid conditions including hypertension, chronic kidney disease, obesity, and depression. Overall, the transported incidence rates of the HOIs to the AD cohort were lower than those obtained in the RA cohort by 13-50%. CONCLUSION: We applied transportability methods to characterize the risk of the HOIs in the AD population and found absolute risks higher than that of the general population. Future work is needed to validate these conclusions in comparable populations.

Accelerating diagnosis of Parkinson's disease through risk prediction.

BACKGROUND: Characterization of prediagnostic Parkinson's Disease (PD) and early prediction of subsequent development are critical for preventive interventions, risk stratification and understanding of disease pathology. This study aims to characterize the role of the prediagnostic period in PD and, using selected features from this period as novel interception points, construct a prediction model to accelerate the diagnosis in a real-world setting. METHODS: We constructed two sets of machine learning models: a retrospective approach highlighting exposures up to 5 years prior to PD diagnosis, and an alternative model that prospectively predicted future PD diagnosis from all individuals at their first diagnosis of a gait or tremor disorder, these being features that appeared to represent the initiation of a differential diagnostic window. RESULTS: We found many novel features captured by the retrospective models; however, the high accuracy was primarily driven from surrogate diagnoses for PD, such as gait and tremor disorders, suggesting the presence of a distinctive differential diagnostic period when the clinician already suspected PD. The model utilizing a gait/tremor diagnosis as the interception point, achieved a validation AUC of 0.874 with potential time compression to a future PD diagnosis of more than 300 days. Comparisons of predictive diagnoses between the prospective and prediagnostic cohorts suggest the presence of distinctive trajectories of PD progression based on comorbidity profiles. CONCLUSIONS: Overall, our machine learning approach allows for both guiding clinical decisions such as the initiation of neuroprotective interventions and importantly, the possibility of earlier diagnosis for clinical trials for disease modifying therapies.

Technology-Enabled Clinical Trials: Transforming Medical Evidence Generation.

The complexity and costs associated with traditional randomized, controlled trials have increased exponentially over time, and now threaten to stifle the development of new drugs and devices. Nevertheless, the growing use of electronic health records, mobile applications, and wearable devices offers significant promise for transforming clinical trials, making them more pragmatic and efficient. However, many challenges must be overcome before these innovations can be implemented routinely in randomized, controlled trial operations. In October of 2018, a diverse stakeholder group convened in Washington, DC, to examine how electronic health record, mobile, and wearable technologies could be applied to clinical trials. The group specifically examined how these technologies might streamline the execution of clinical trial components, delineated innovative trial designs facilitated by technological developments, identified barriers to implementation, and determined the optimal frameworks needed for regulatory oversight. The group concluded that the application of novel technologies to clinical trials provided enormous potential, yet these changes needed to be iterative and facilitated by continuous learning and pilot studies.

Using Digital Health Technology to Better Generate Evidence and Deliver Evidence-Based Care.

As we enter the information age of health care, digital health technologies offer significant opportunities to optimize both clinical care delivery and clinical research. Despite their potential, the use of such information technologies in clinical care and research faces major data quality, privacy, and regulatory concerns. In hopes of addressing both the promise and challenges facing digital health technologies in the transformation of health care, we convened a think tank meeting with academic, industry, and regulatory representatives in December 2016 in Washington, DC. In this paper, we summarize the proceedings of the think tank meeting and aim to delineate a framework for appropriately using digital health technologies in healthcare delivery and research.

In Vivo Optical Imaging of Myelination Events in a Myelin Basic Protein Promoter-Driven Luciferase Transgenic Mouse Model.

The compact myelin sheath is important for axonal function, and its loss can lead to neuronal cell death and irreversible functional deficits. Myelin is vulnerable to a variety of metabolic, toxic, and autoimmune insults. In diseases like multiple sclerosis, there is currently no therapy to stop myelin loss, underscoring the need for neuroprotective and remyelinating therapies. Noninvasive, robust techniques are also needed to confirm the effect of such therapies in animal models. This article describes the generation, characterization, and potential uses for a myelin basic protein-luciferase (MBP-luci) transgenic mouse model, in which the firefly luciferase reporter gene is selectively controlled by the MBP promoter. In vivo bioluminescence imaging can be used to visualize and quantify demyelination and remyelination at the transcriptional level, noninvasively, and in real time. Transgenic mice were assessed in the cuprizone-induced model of demyelination, and luciferase activity highly correlated with demyelination and remyelination events as confirmed by both magnetic resonance imaging and postmortem histological analysis. Furthermore, MBP-luci mice demonstrated enhanced luciferase signal and remyelination in the cuprizone model after treatment with a peroxisome proliferator activated receptor-delta selective agonist and quetiapine. Imaging sensitivity was further enhanced by using CycLuc 1, a luciferase substrate, which has greater blood-brain barrier penetration. We demonstrated the utility of MBP-luci model in tracking myelin changes in real time and supporting target and therapeutic validation efforts.

In vitro myelin formation using embryonic stem cells.

Myelination in the central nervous system is the process by which oligodendrocytes form myelin sheaths around the axons of neurons. Myelination enables neurons to transmit information more quickly and more efficiently and allows for more complex brain functions; yet, remarkably, the underlying mechanism by which myelination occurs is still not fully understood. A reliable in vitro assay is essential to dissect oligodendrocyte and myelin biology. Hence, we developed a protocol to generate myelinating oligodendrocytes from mouse embryonic stem cells and established a myelin formation assay with embryonic stem cell-derived neurons in microfluidic devices. Myelin formation was quantified using a custom semi-automated method that is suitable for larger scale analysis. Finally, early myelination was followed in real time over several days and the results have led us to propose a new model for myelin formation.

High-content phenotypic screening and triaging strategy to identify small molecules driving oligodendrocyte progenitor cell differentiation.

Multiple Sclerosis is a demyelinating disease of the CNS and the primary cause of neurological disability in young adults. Loss of myelinating oligodendrocytes leads to neuronal dysfunction and death and is an important contributing factor to this disease. Endogenous oligodendrocyte precursor cells (OPCs), which on differentiation are responsible for replacing myelin, are present in the adult CNS. As such, therapeutic agents that can stimulate OPCs to differentiate and remyelinate demyelinated axons under pathologic conditions may improve neuronal function and clinical outcome. We describe the details of an automated, cell-based, morphometric-based, high-content screen that is used to identify small molecules eliciting the differentiation of OPCs after 3 days. Primary screening was performed using rat CG-4 cells maintained in culture conditions that normally support a progenitor cell-like state. From a library of 73,000 diverse small molecules within the Sanofi collection, 342 compounds were identified that increased OPC morphological complexity as an indicator of oligodendrocyte maturation. Subsequent to the primary high-content screen, a suite of cellular assays was established that identified 22 nontoxic compounds that selectively stimulated primary rat OPCs but not C2C12 muscle cell differentiation. This rigorous triaging yielded several chemical series for further expansion and bio- or cheminformatics studies, and their compelling biological activity merits further investigation.

Complementary patterns of gene expression by human oligodendrocyte progenitors and their environment predict determinants of progenitor maintenance and differentiation.

OBJECTIVE: Glial progenitor cells are abundant in adult human white matter. This study was designed to identify signaling pathways regulating their self-renewal and fate. METHODS: We compared the transcriptional profiles of freshly sorted adult human white matter progenitor cells (WMPCs), purified by A2B5-based immunomagnetic sorting, with those of the white matter from which they derived. RESULTS: We identified 132 genes differentially expressed by WMPCs; these included principal components of five receptor-defined signaling pathways, represented by platelet derived growth factor receptor alpha (PDGFRA) and type 3 fibroblast growth factor receptor (FGFR3), receptor tyrosine phosphatase-beta/zeta (RTPZ), notch, and syndecan3. WMPCs also differentially expressed the bone morphogenetic protein 4 (BMP4) inhibitors neuralin and BAMBI (BMP and activin membrane-bound inhibitor), suggesting tonic defense against BMP signaling. Differential overexpression of RTPZ was accompanied by that of its modulators pleiotrophin, NrCAM, tenascin, and the chondroitin sulfate proteoglycans, suggesting the importance of RTPZ signaling to WMPCs. When exposed to the RTPZ inhibitor bpV(phen), or lentiviral-shRNAi against RTPZ, WMPCs differentiated as oligodendrocytes. Conversely, when neuralin and BAMBI were antagonized by BMP4, astrocytic differentiation was induced, which was reversible by noggin. INTERPRETATION: The RTPZ and BMP pathways regulate the self-maintenance of adult human WMPCs, and can be modulated to induce their oligodendrocytic or astrocytic differentiation. As such, they provide targets by which to productively mobilize resident progenitor cells of the adult human brain.

17beta-estradiol protects oligodendrocytes from cytotoxicity induced cell death.

During pregnancy, changes in circulating levels of hormones, including estrogens, correlates with a significant decrease in the relapse incidence in women with Multiple Sclerosis (MS). In the present study, we demonstrate that both primary and cell line cultures of rat oligodendrocytes express the estrogen receptor (ER)-alpha and ERbeta estrogen receptors in the cytosol and nucleus, and that nuclear compartmentalization becomes more pronounced as the cells mature. Moreover, 17beta-estradiol significantly decreases the cytotoxic effects of the peroxynitrite generator 3-(4-morpholinyl)-sydnonimine (SIN-1) in both immature and mature oligodendrocytes in a dose dependent manner. This protective mechanism requires pretreatment with 17beta-estradiol and is blocked by ICI 182,780, a selective ERalpha/ERbeta antagonist. These results strongly suggest that 17beta-estradiol protects oligodendrocytes against SIN-1 mediated cytotoxicity through the activation of the estrogen receptors and provides new insights into the roles of the estrogen signaling pathways in myelin forming cells that are lost in demyelinating disorders.