Improving the in silico assessment of pathogenicity for compensated variants

Understanding the functional sequelae of amino-acid replacements is of fundamental importance in medical genetics. Perhaps, the most intuitive way to assess the potential pathogenicity of a given human missense variant is by measuring the degree of evolutionary conservation of the substituted amino-acid residue, a feature that generally serves as a good proxy metric for the functional/structural importance of that residue. However, the presence of putatively compensated variants as the wild-type alleles in orthologous proteins of other mammalian species not only challenges this classical view of amino-acid essentiality but also precludes the accurate evaluation of the functional impact of this type of missense variant using currently available bioinformatic prediction tools. Compensated variants constitute at least 4% of all known missense variants causing human-inherited disease and hence represent an important potential source of error in that they are likely to be disproportionately misclassified as benign variants. The consequent under-reporting of compensated variants is exacerbated in the context of next-generation sequencing where their inappropriate exclusion constitutes an unfortunate natural consequence of the filtering and prioritization of the very large number of variants generated. Here we demonstrate the reduced performance of currently available pathogenicity prediction tools when applied to compensated variants and propose an alternative machine-learning approach to assess likely pathogenicity for this particular type of variant

Specifying and Validating Probabilistic Inputs for Prescriptive Models of Decision Making over Time

Optimization models for making decisions over time in uncertain environments rely on probabilistic inputs, such as scenario trees for stochastic mathematical programs. The quality of model outputs, i.e., the solutions obtained, depends on the quality of these inputs. However, solution quality is rarely assessed in a rigorous way. The connection between validation of model inputs and quality of the resulting solution is not immediate. This chapter discusses some efforts to formulate realistic probabilistic inputs and subsequently validate them in terms of the quality of solutions they produce. These include formulating probabilistic models based on statistical descriptions understandable to decision makers; conducting statistical tests to assess the validity of stochastic process models and their discretization; and conducting re-enactments to assess the quality of the formulation in terms of solution performance against observational data. Studies of long-term capacity expansion in service industries, including electric power, and short-term scheduling of thermal electricity generating units provide motivation and illustrations. The chapter concludes with directions for future research

Availability, accessibility and delivery to patients of the 28 orphan medicines approved by the European Medicine Agency for hereditary metabolic diseases in the MetabERN network.

The European Medicine Agency granted marketing approval to 164 orphan medicinal products for rare diseases, among which 28 products intended for the treatment of hereditary metabolic diseases. Taking advantage of its privileged connection with 69 healthcare centres of excellence in this field, MetabERN, the European Reference Network for hereditary metabolic diseases, performed a survey asking health care providers from 18 European countries whether these products are available on the market, reimbursed and therefore accessible for prescription, and actually delivered in their centre. RESULTS: Responses received from 52 centres (75%) concerned the design of treatment plans, the access to marketed products, and the barriers to delivery. Treatment options are always discussed with patients, who are often involved in their treatment plan. Most products (26/28) are available in most countries (15/18). Among the 15 broadly accessible products (88.5% of the centres), 9 are delivered to most patients (mean 70.1%), and the others to only few (16.5%). Among the 10 less accessible products (40.2% of the centres), 6 are delivered to many patients (66.7%), and 4 are rarely used (6.3%). Information was missing for 3 products. Delay between prescription and delivery is on average one month. Beside the lack of availability or accessibility, the most frequent reasons for not prescribing a treatment are patients' clinical status, characteristic, and personal choice. CONCLUSIONS: Data collected from health care providers in the MetabERN network indicate that two-third of the orphan medicines approved by EMA for the treatment of hereditary metabolic diseases are accessible to treating patients, although often less than one-half of the patients with the relevant conditions actually received the approved product to treat their disease. Thus, in spite of the remarkable achievement of many products, patients concerned by EMA-approved orphan medicinal products have persistent unmet needs, which deserve consideration. The enormous investments made by the companies to develop products, and the high financial burden for the Member States to purchase these products emphasize the importance of a scrupulous appreciation of treatment value involving all stakeholders at early stage of development, before marketing authorization, and during follow up