Pivotal Role of Translation in Anti-Infective Development.

The value of model-based translation in drug discovery and development is now effectively being recognized in many disease areas and among various stakeholders. Such quantitative approaches are expected to facilitate the selection on which compound to prioritize for successful development, predict the human efficacious dose based on preclinical data with adequate precision, guide design, and de-risk later development stages. The importance of time-dependencies, which are typically species-dependent due to different turnover rates of biological processes, is, however, often neglected. For bacterial infections, the choice of dosing regimen is typically relying on preclinical pharmacokinetic (PK) and pharmacodynamic (PD) data, because the bacterial load and disease severity, and consequently the PK/PD relationship, cannot be quantified well on clinical data, given the low-information end points used. It is time to recognize the limitations of using time-collapsed approaches for translation (i.e., methods where targets are based on summary measures of exposure and response). Models describing the full time-course captures important quantitative information of drug distribution, bacterial growth, antibiotic killing, and resistance development, and can account for species-differences in the PK profiles driving the killing. Furthermore, with a model-based approach for translation, we can take a holistic approach in development of a joint model for in vitro, in vivo, and clinical data, as well as incorporating information on the contribution of the immune system. Such advancements are anticipated to facilitate rational decision making during various stages of drug development and in the optimization of treatment regimens for different groups of patients.

GLP: A requirement in cell therapies - perspectives for the cardiovascular field.

In biomedical research, enormous progress is being made and new candidates for putative medicinal products emerge. However, most published preclinical data are not conducted according to the standard Good Laboratory Practice (GLP). GLP is mandatory for preclinical analysis of Advanced Therapy Medicinal Products (ATMP) and thereby a prerequisite for planning and conduction of clinical trials. Not inconsiderable numbers of clinical trials are terminated earlier or fail - do inadequate testing strategies or missing specialized assays during the preclinical development contribute to this severe complex of problems? Unfortunately, there is also a lack of access to GLP testing results and OECD (Organisation for Economic Co-operation and Development) GLP guidelines are not yet adjusted to ATMP specialties. Ultimately, GLP offers possibilities to generate reliable and reproducible data. Therefore, this review elucidates different GLP aspects in drug development, speculates on reasons of putative low GLP acceptance in the scientific community and mentions solution proposals.

Issues in the future development of new analgesic drugs.

PURPOSE OF REVIEW: There is a clear unmet need for either the development of new drugs for the treatment of painful pathologies or the better use of the existing agents denoted by the lack of efficacy of many existing drugs in a number of patients, limitations of their use due to severity of side effects, and by the high number of drugs that fail to reach clinical efficacy from preclinical development. This account considers the efforts being made to better validate new analgesic components and to improve translational efficacy of existing drugs. RECENT FINDINGS: A better use of the available models and tools can improve the predictive validity of new analgesic drugs, as well as using intermediate steps when translating drugs to clinical context such as characterizing drugs using stem cell-sensory derived neurones. Profiling patient sensory phenotypes can decrease the number of failed clinical trials and improve patient outcome. SUMMARY: An integrative approach, comprising the use of complementary techniques to fully characterize drug profiles, is necessary to improve translational success of new analgesics.

[Developability assessment with case studies highlighting the decision taking]. / Exemples d'études de développabilité apportant un éclairage à la prise de décision.

Therapeutic antibodies generation has to be faster with less development costs. This requires combination of in silico predictions associated with cutting edge screening and characterization technologies. Here, non-exhaustive examples illustrate this simultaneity need.

TITLE: Exemples d'études de développabilité apportant un éclairage à la prise de décision. ABSTRACT: De nos jours, la génération d'anticorps thérapeutiques doit être plus rapide avec des coûts de développement moins importants. Pour cela, des prédictions in silico sont associées à des technologies de criblage et de caractérisation de pointe. Les exemples choisis ici sont non-exhaustifs mais illustrent ce besoin de travailler en parallèle.

Lessons learned from Ebola Vaccine R&D during a public health emergency.

In spite of a complete lack of Research and Development (R&D) preparedness, the 2013-2016 West-Africa Ebola experience demonstrated that it is possible to compress R&D timelines to less than a single year, from a more usual decade or longer. This is mostly to be credited to an unprecedented collaborative effort building on the availability of a small number of candidate diagnostic tests, drugs and vaccines that could be moved rapidly into the clinical phase evaluation. The World Health Organization (WHO) led international consultations and activities - including the organization of a successful Ebola vaccine efficacy trial in Guinea - as a contribution to the unprecedented global efforts to control the Ebola epidemic. Since 2015, WHO expert teams and partners are implementing a novel R&D model for emerging infectious pathogens which are the most likely to cause severe outbreaks in the future, and for which no or only few medical countermeasures are available: the WHO R&D Blueprint. The objective for the Blueprint is the fostering of a R&D environment which is prepared for quickly and effectively responding to outbreaks due to emerging infectious disease.