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Certain medications can impair driving because of side effects such as drowsiness, dizziness, or decreased concentration. In Europe, this risk is chiefly assessed using the “Driving under the Influence of Drugs, Alcohol, and Medications” (DRUID) system — an EU-promoted framework from 2006 to 2012 in which the University of Valladolid, Valladolid, Spain, participated — which sorts drugs into four categories: zero (no effect on driving), one (slight effect), two (moderate effect), and three (severe effect on driving).

Vicente Traver Salcedo, PhD, director of the Technological Innovation for Health and Well-Being (SABIEN) group at the University Institute of Information and Communication Technologies (ITACA), Polytechnic University of Valencia (UPV), Valencia, Spain, and coauthor Salvador Borja Ripoll wrote in their introduction for Univadis Spain, which is part of the Medscape Professional Network, that the DRUID system’s qualitative grouping of medications into broad categories limits its reproducibility, clinical applicability, and ability to distinguish between drugs within the same category; it does not define proportional distances between categories (so moving from one to two should not be interpreted as a “double” risk, nor is the jump from two to three necessarily equivalent to that from zero to one), and many drugs remain unassigned because they have not been evaluated, making it difficult to keep the classification fully up-to-date.

For this reason, researchers from the SABIEN group led a study in which a new tool was developed to enable a more precise classification that is more useful in clinical practice, the mobility and risk basis factor (FMB) scale. The results were recently published in the journal Therapeutic Advances in Drug Safety. The main objective of developing this scale is to provide clearer and more useful information for both healthcare professionals and patients. Instead of assigning just a single category, the scale combines different factors to generate a continuous index that more accurately reflects risk under real-world conditions.

The FMB Scale

This scale allows for the assignment of an individualized score to each marketed medication and can also assess a patient’s entire treatment regimen. It also addresses the issue of drugs that currently appear as unassessed in the DRUID system.

To develop it, the team started with the DRUID system “because we consider it to have been a very important benchmark at the European level and a good starting point for evaluating the influence of medications on driving,” the study authors explained.

Specifically, “We followed a multifactorial methodology that integrates various parameters such as the DRUID category, adverse reactions and their frequencies, number of adverse drug reactions, marketed doses, dosage form, or treatment phase. To do this, we analyzed all medications marketed in Spain. In this way, we sought to take it a step further by incorporating a more precise and quantifiable assessment. Our unique contribution is that it allows us to distinguish between medications that appear within the same risk category in the DRUID system. In other words, it allows us to identify which of them may have a greater potential impact on driving based on the parameters used,” Traver and Borja, the study’s first author, said.

Medications’ Driving Risk Scores

Many medications can substantially impair driving, particularly those that cause neurologic adverse effects, sedation, or psychomotor impairment. Researchers from the SABIEN group highlighted several notable examples: valproic acid in injectable form (FMB value, 3.3889) for antiepileptic treatment, alprazolam oral solution (FMB value, 3.3753), and oxcarbazepine (FMB value, 3.3439), all linked to central nervous system effects, and pethidine (FMB value, 3.3417), an opioid analgesic. At the other end of the scale, they noted numerous drugs with negligible impact: for instance, paracetamol at its maximum 1-g dose has an FMB value of 0.7590 as do many topical preparations (creams or ointments) that essentially do not affect driving, Traver and Borja said.

Comparison of FMB With DRUID

In most cases, there is a consistent correlation with the DRUID system. Medications classified in high DRUID categories also tend to have high FMB scores. However, “FMB introduces a more granular and continuous assessment. This means there are specific cases where some medications appear with scores slightly higher or lower than the traditional DRUID classification. In part, this may be because our scale incorporates more detailed and up-to-date information on adverse reactions and data accumulated over years of pharmacovigilance following the initial development of the DRUID system,” the FMB authors explained.

Implementation of FMB

To facilitate the future implementation of the FMB scale, it was designed “to maintain a correlation with the philosophy of the DRUID system, so that the scores retain an intuitive equivalence with their traditional categories. This facilitates interpretation and potential future integration with existing tools and classifications,” the authors noted.

The stated objective of the UPV researchers “is to facilitate the gradual integration of the FMB scale into tools commonly used in healthcare. To achieve this, it is essential to establish avenues for collaboration with both public health entities and other stakeholders in the healthcare and technology sectors, in order to explore its potential incorporation into electronic prescription systems, pharmacy dispensing software, and health apps. The idea is that healthcare professionals, such as doctors and pharmacists, can have more accurate information when prescribing or dispensing a medication and thus prescribe the most appropriate one based on the patient’s profile. Similarly, it could allow patients themselves to receive alerts regarding the potential impact of their treatment on driving,” Traver and Borja concluded.

The authors of this study declared that they had no conflicts of interest.

The study authors wish to pay special tribute to the memory of Ferran Mocholí, one of the co-authors, an ITACA researcher, whose vision at the start of the project was key to the development of the FMB scale.

This story was translated from Univadis Spain, part of the Medscape Professional Network.