Good machine learning practices: Learnings from the modern pharmaceutical discovery enterprise.

Machine Learning (ML) and Artificial Intelligence (AI) have become an integral part of the drug discovery and development value chain. Many teams in the pharmaceutical industry nevertheless report the challenges associated with the timely, cost effective and meaningful delivery of ML and AI powered solutions for their scientists. We sought to better understand what these challenges were and how to overcome them by performing an industry wide assessment of the practices in AI and Machine Learning. Here we report results of the systematic business analysis of the personas in the modern pharmaceutical discovery enterprise in relation to their work with the AI and ML technologies. We identify 23 common business problems that individuals in these roles face when they encounter AI and ML technologies at work, and describe best practices (Good Machine Learning Practices) that address these issues.

Patient listening on social media for patient-focused drug development: a synthesis of considerations from patients, industry and regulators.

Patients, life science industry and regulatory authorities are united in their goal to reduce the disease burden of patients by closing remaining unmet needs. Patients have, however, not always been systematically and consistently involved in the drug development process. Recognizing this gap, regulatory bodies worldwide have initiated patient-focused drug development (PFDD) initiatives to foster a more systematic involvement of patients in the drug development process and to ensure that outcomes measured in clinical trials are truly relevant to patients and represent significant improvements to their quality of life. As a source of real-world evidence (RWE), social media has been consistently shown to capture the first-hand, spontaneous and unfiltered disease and treatment experience of patients and is acknowledged as a valid method for generating patient experience data by the Food and Drug Administration (FDA). While social media listening (SML) methods are increasingly applied to many diseases and use cases, a significant piece of uncertainty remains on how evidence derived from social media can be used in the drug development process and how it can impact regulatory decision making, including legal and ethical aspects. In this policy paper, we review the perspectives of three key stakeholder groups on the role of SML in drug development, namely patients, life science companies and regulators. We also carry out a systematic review of current practices and use cases for SML and, in particular, highlight benefits and drawbacks for the use of SML as a way to identify unmet needs of patients. While we find that the stakeholders are strongly aligned regarding the potential of social media for PFDD, we identify key areas in which regulatory guidance is needed to reduce uncertainty regarding the impact of SML as a source of patient experience data that has impact on regulatory decision making.

Inflammation and cardiovascular status impact midazolam pharmacokinetics in critically ill children: An observational, prospective, controlled study.

Altered physiology caused by critical illness may change midazolam pharmacokinetics and thereby result in adverse reactions and outcomes in this vulnerable patient population. This study set out to determine which critical illness-related factors impact midazolam pharmacokinetics in children using population modeling. This was an observational, prospective, controlled study of children receiving IV midazolam as part of routine care. Children recruited into the study were either critically-ill receiving continuous infusions of midazolam or otherwise well, admitted for elective day-case surgery (control) who received a single IV bolus dose of midazolam. The primary outcome was to determine the population pharmacokinetics and identify covariates that influence midazolam disposition during critical illness. Thirty-five patients were recruited into the critically ill arm of the study, and 54 children into the control arm. Blood samples for assessing midazolam and 1-OH-midazolam concentrations were collected opportunistically (critically ill arm) and in pre-set time windows (control arm). Pharmacokinetic modeling demonstrated a significant change in midazolam clearance with acute inflammation (measured using C-Reactive Protein), cardio-vascular status, and weight. Simulations predict that elevated C-Reactive Protein and compromised cardiovascular function in critically ill children result in midazolam concentrations up to 10-fold higher than in healthy children. The extremely high concentrations of midazolam observed in some critically-ill children indicate that the current therapeutic dosing regimen for midazolam can lead to over-dosing. Clinicians should be aware of this risk and intensify monitoring for oversedation in such patients.

Validation of methods for determining pediatric midazolam using wet whole blood and volumetric absorptive microsampling.

Aim: Collection and quantitative analysis in dry blood using volumetric absorptive microsampling (VAMS™) potentially offers significant advantages over conventional wet whole blood analysis. This manuscript explores their use for pediatric sampling and explores additional considerations for the validation of the bioanalytical method. Results: HPLC-MS/MS methods for the determination of midazolam and its major metabolite 1-OH midazolam in both whole wet blood, and dry blood collected on VAMS were developed, validated, and used to support an observational clinical study to compare pharmacokinetic parameters in pediatric patients. Conclusion: Validation data met internationally accepted guideline criteria. A strong correlation was observed in calculated concentrations between wet and dry test samples, indicating that VAMS is a suitable technique for use in pediatric clinical studies.