BackgroundBetter understanding and prediction of PD progression could improve disease management and clinical trial design. We aimed to use longitudinal clinical, molecular, and genetic data to develop predictive models, compare potential biomarkers, and identify novel predictors for motor progression in PD. We also sought to assess the use of these models in the design of treatment trials in PD.MethodsA Bayesian multivariate predictive inference platform was applied to data from the Parkinson’s Progression Markers Initiative (PPMI) study (NCT01141023). We used genetic data and baseline molecular and clinical variables from PD patients and healthy controls to construct an ensemble of models to predict the annualised rate of the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale parts II and III combined. We tested our overall explanatory power, as assessed by the coefficient of determination (R2), and replicated novel findings in an independent clinical cohort of PD patients from the Longitudinal and Biomarker Study in PD (LABS-PD; NCT00605163). The potential utility of these models for clinical trial design was quantified by comparing simulated randomized placebo-controlled trials within the out-of sample LABS-PD cohort.FindingsA total of 117 controls and 312 PD cases were available for analysis. Our model ensemble exhibited strong performance in-cohort (5-fold cross-validated R2=41%, 95% CI: 35% – 47%) and significant, though reduced, performance out-of-cohort (R2=9%, 95% CI: 4% – 16%). Individual predictive features identified from PPMI data were confirmed in the LABS-PD cohort of 317 PD patients. These included significant replication of higher baseline motor score, male sex, and increased age, as well as a novel PD-specific epistatic interaction all indicative of faster motor progression. Genetic variation was the most useful predictive marker of motor progression (2.9%, 95%CI: 1.5–4.3%). CSF biomarkers at baseline showed a more modest (0.3%; 95%CI: 0.1–0.5%), but still significant effect on motor progression prediction. The simulations (n=5000) showed that incorporating the predicted rates of motor progression into the final models of treatment effect reduced the variability in the study outcome allowing significant differences to be detected at sample sizes up to 20% smaller than in naïve trials.InterpretationOur model ensemble confirmed established and identified novel predictors of PD motor progression. Improving existing prognostic models through machine learning approaches should benefit trial design and evaluation, as well as clinical disease monitoring and treatment.FundingMichael J. Fox Foundation for Parkinson’s Research and National Institute of Neurological Disorders and Stroke (1P20NS092529-01).

No abstract

Explicitly identifying the genome of a host organism including sequencing, mapping, and annotating its genetic code has become a priority in the field of biotechnology with aims at improving the efficiency and understanding of cell culture bioprocessing. Recombinant protein therapeutics, primarily produced in mammalian cells, constitute a $108 billion global market. The most common mammalian cell line used in biologic production processes is the Chinese hamster ovary (CHO) cell line, and although great improvements have been made in titer production over the past 25 years, the underlying molecular and physiological factors are not well understood. Confident understanding of CHO bioprocessing elements (e.g. cell line selection, protein production, and reproducibility of process performance and product specifications) would significantly improve with a well understood genome. This review describes mammalian cell culture use in bioprocessing, the importance of obtaining CHO cell line genetic sequences, and the current status of sequencing efforts. Furthermore, transcriptomic techniques and gene expression tools are presented, and case studies exploring genomic techniques and applications aimed to improve mammalian bioprocess performance are reviewed. Finally, future implications of genomic advances are surmised.