Preclinical risk assessment strategy to mitigate the T-cell dependent immunogenicity of protein biotherapeutics: State of the art, challenges and future perspectives.

Protein therapeutics hold a prominent role and have brought significant diversity in efficacious medicinal products. Not just monoclonal antibodies and different antibody formats (pegylated antigen-binding fragments, bispecifics, antibody-drug conjugates, single chain variable fragments, nanobodies, dia-, tria- and tetrabodies), but also purified blood products, growth factors, recombinant cytokines, enzyme replacement factors, fusion proteins are all good instances of therapeutic proteins that have been developed in the past decades and approved for their value in oncology, immune-oncology, and autoimmune diseases discovery programs. Although there was an ingrained belief that fully humanized proteins were expected to have limited immunogenicity, adverse effects associated with immune responses to biological therapies raised some concern in biotech companies. Consequently, drug developers are designing strategies to assess potential immune responses to protein therapeutics during both the preclinical and clinical phases of development. Despite the many factors that can contribute to protein immunogenicity, T cell- (thymus-) dependent (Td) immunogenicity seems to play a crucial role in the development of anti-drug antibodies (ADAs) to biologics. A broad range of methodologies to predict and rationally assess Td immune responses to protein drugs has been developed. This review aims to briefly summarize the preclinical immunogenicity risk assessment strategy to mitigate the risk of potential immunogenic candidates coming towards clinical phases, discussing the advantages and limitations of these technologies, and suggesting a rational approach for assessing and mitigating Td immunogenicity.

[Risk of inappropriateness and efficiency assessment of hospitals]. / Ricoveri "a rischio di inappropriatezza" e valutazione dell'efficienza tecnica.

We evaluated with the Data Envelopment Analysis (DEA) 13 decision making units (DMU) at IDI -IRCCS for the years 2000 and 2001. Input variables were: cost for medical personnel, cost for non medical personnel and number of beds; output variables was the number of discharged patients weighted with DRG. Later in a second model we delete the cases considered to be at "high risk" to be inappropriate for treatment as inpatients. DEA instrument is confirmed useful in the efficiency evaluation for DMU at hospital level, ranking were different between the two models. The Health Direction can utilise the analysis to understand reasons of inefficiency and for incentive policy.