Regulatory Considerations on the use of Machine Learning based tools in Clinical Trials.

Background: The widespread increasing use of machine learning (ML) based tools in clinical trials (CTs) impacts the activities of Regulatory Agencies (RAs) that evaluate the development of investigational medicinal products (IMPs) in clinical studies to be carried out through the use of data-driven technologies. The fast progress in this field poses the need to define new approaches and methods to support an agile and structured assessment process. Method: The assessment of key information, characteristics and challenges deriving from the application of ML tools in CTs and their link with the principles for a trustworthy artificial intelligence (AI) that directly affect the decision-making process is investigated. Results: Potential issues are identified during the assessment and areas of greater interaction combining key regulatory points and principles for a trustworthy AI are highlighted. The most impacted areas are those related to technical robustness and safety of the ML tool, in relation to data used and the level of evidence generated. Additional areas of attention emerged, like the ones related to data and algorithm transparency. Conclusion: We evaluate the applicability of a new method to further support the assessment of medicinal products developed using data-driven tools in a CT setting. This is a first step and new paradigms should be adopted to support policy makers and regulatory decisions, capitalizing on technology advancements, considering stakeholders' feedback and still ensuring a regulatory framework on safety and efficacy.

Clinical Trials and Machine Learning: Regulatory Approach Review.

Machine Learning, a fast-growing technology, is an application of Artificial Intelligence that has provided important contributes to drug discovery and clinical development. In the last few years, the number of clinical applications based on Machine Learning has been constantly growing and this is now affecting the National Competent Authorities during the assessment of most recently submitted Clinical Trials that are designed, managed or that are generating data deriving from the use of Machine Learning or Artificial Intelligence technologies. We review current information available on the regulatory approach to Clinical Trials and Machine Learning. We also provide inputs for further reasoning and potential indications, including six actionable proposals for regulators to proactively drive the upcoming evolution of Clinical Trials within a strong regulatory framework, focusing on patient's safety, health protection and fostering immediate access to effective treatments.

Immediate versus delayed surgical intervention for reconstructive therapy of HIV-associated facial lipoatrophy: a randomized open-label study.

We assessed the safety and efficacy of reconstructive therapy with facial fillers for the treatment of HIV-associated facial lipoatrophy (FLA) through a randomized, controlled, open-label single-center study. A total of 134 HIV-infected patients with severe FLA were randomly assigned to receive immediate (67 patients) or delayed (67 patients) facial injections of poly-l-lactic acid (PLA) or polyacrylamide gel (PAIG). Outcome measures included changes in physician and patient FLA severity scale, adverse events, and changes in health-related quality of life (HRQoL) and anxiety using validated measures. The mean average study follow-up was 27 weeks for the immediate and 25 weeks for the delayed subjects. Adverse events were mild and resolved after a mean of 4 days. Compared to patients randomized to the delayed treatment group, patients assigned to the immediate treatment group had significantly lower physician-rated (0.0 versus -3.0; p < 0.0001) and patient-rated (0.1 versus -1.8; p < 0.0001) FLA severity scores. By contrast, measures exploring HRQoL and anxiety did not show any significant difference between patients randomized to the immediate and deferred groups. Reconstructive therapy with facial fillers was effective and safe and led to significant improvements in FLA severity. However, no significant gains in HRQoL, relational and psychological consequences of body changes, and anxiety-related concerns were observed. Studies should be performed to identify patients who could maximally benefit from filling interventions for FLA.

Health-related quality of life outcomes in HIV-infected patients starting different combination regimens in a randomized multinational trial: the INITIO-QoL substudy.

The health-related quality of life (HRQoL) outcomes in HIV-infected, treatment-naive patients starting different HAART regimens in a 3-year, randomized, multinational trial were compared. HRQoL was measured in a subgroup of patients enrolled in the INITIO study (153/911), using a modified version of the MOS-HIV questionnaire. The regimens compared in the INITIO trial were composed by two NRTIs (didanosine + stavudine) plus either an NNRTI (efavirenz) or a PI (nelfinavir), or both (efavirenz + nelfinavir). Primary HRQoL outcomes were Physical and Mental Health Summary scores (PHS and MHS, respectively). During follow-up, an increase of PHS score was observed in all treatment arms. The MHS score remained substantially unchanged with the four-drug combination and showed with both NNRTI- and PI-based three-drug regimens a marked trend toward improvement, which became statistically significant when a multiple imputation method was used to adjust for missing data. Overall, starting all the combination regimens compared in the INITIO study was associated with a maintained or slightly improved HRQOL status, consistently with the positive immunological and virological changes observed in the main study. The observed differences in the MHS indicate a possible HRQoL benefit associated to the use of three-drug, two-class regimens and no additional benefit for the use of four-drug, three-class regimens, confirming that three-drug, two-class regimens that include two NRTIs plus either an NNRTI or a PI should be preferred as initial treatment of HIV infection.

[Prevalence of the use of old and new drugs among new entrants in Italian prisons]. / "Prevalenza" di uso di vecchie e nuove droghe nei nuovi ingressi in strutture penitenziarie italiane.

An epidemiological survey was conducted to evaluate the prevalence of drug use among new entrants in Italian prisons. Overall, 1267 inmates were recruited by 9 prisons. Drug use before imprisonment was reported by 55.8% of the participants. Cocaine was the most commonly used drug (42%), followed by heroin (34%), marijuana/cannabis (33%), ecstasy (7%), hallucinogens (6%), amphetamines (5%); more than one drug was reported by 68% of abusers. Recent use (1 month before imprisonment) was admitted by up to 27% of inmates. Alcohol or tobacco use was reported by 38 and 77% of the inmates, respectively. Our findings indicate that a high proportion of inmates has ever used drugs; adequate intervention is needed to reduce the risk of addictive behaviour in this population group.