(T1430-05-29) Real-World Comedication in Cognitive Impairment Associated with Schizophrenia (CIAS): Translating Data into Drug-Drug Interaction (DDI)-Aware, Patient-Centered CNS Drug Development

Purpose: Schizophrenia affects approximately 24 million people worldwide and is characterized by three main symptom domains: positive (e.g., hallucinations, delusions), negative (e.g., social withdrawal, lack of motivation), and cognitive (e.g., impaired memory, slowed thinking, and difficulty organizing thoughts) ^1,2. Cognitive symptoms often emerge before the onset of psychosis and typically persist even after other symptoms are managed. These deficits are collectively referred to as Cognitive Impairment Associated with Schizophrenia (CIAS) affecting over 80% of individuals with schizophrenia and are tightly linked to long-term disability and reduced quality of life ^3,4. In clinical practice, pharmacological treatments such as antipsychotics, anticholinergics, and benzodiazepines are commonly prescribed to manage symptoms and prevent further deterioration⁵. As with many pharmacological interventions, DDI can be a serious concern, especially in CIAS patients on more than one therapy. Therefore, during clinical development of new drugs designed for the treatment of CIAS, it is very important to consider concomitant medication therapy management^6,7. Proactive DDI risk mitigation, informed by real-world data, can optimize trial design and support personalized medication strategies. This research leverages real-world data (RWD) to characterize concomitant medication use in patients with CIAS and assess the potential for drug-drug interactions (DDIs), with the goal of informing CIAS clinical trial design and regulatory strategies. These findings can inform safer and more effective trial designs, inclusion/exclusion criteria and treatment regimens, ultimately enhancing patient care and drug safety⁶.

Methods: This research utilized the Optum® database, which includes U.S. based commercial and Medicare Advantage insurance claims, to identify the prevalence of medication use among patients ( >=18 years old) diagnosed with CIAS from January 1, 2019, to December 31, 2024. A targeted literature review was conducted to evaluate the drug-drug interaction (DDI) potential of commonly prescribed medications in this patient population. The review focused on cytochrome P450 (CYP), UGT enzymes and transporter-mediated interactions using sources such as the Certara DDI database, PubMed, Drug interaction Flockhart tables (Indiana University) and the FDA’s online DDI tables.

Results: Over a five-year period, 3,137 patients with a diagnosis of CIAS were identified in the Optum® dataset (USA) and represented in Figure 1. The median age was 48 years (range: 18–64), with a nearly equal gender distribution (49.5% female). The racial composition included 62.9% Caucasian, 29.1% Black, 2.86% Asian, and 5.20% unknown. This demographic breakdown highlights the diversity of the CIAS population captured in real-world clinical settings. Using the Optum® database, a comprehensive list of approximately 1,000 concomitant medications frequently prescribed to CIAS patients was compiled. This dataset includes both in vitro and in vivo information on metabolic pathways involving CYP and UGT enzymes, as well as uptake (OATP1B, OAT, OCT) and efflux (P-gp, BCRP, MATE) transporters. However, for the purposes of the current research, only the in vivo data were analyzed. Analysis of medication patterns revealed the top 10 most commonly prescribed concomitant medications among CIAS patients. These medications were further evaluated for their potential to contribute to drug-drug interactions (DDIs), with each classified as either a DDI object or precipitant. The findings, summarized in Table 1, underscore the importance of careful medication management in this population, particularly in the context of CNS drug development and trial design.

Conclusion: The top 10 most common concomitant medications in CIAS are listed in Table 1, indicating their DDI characteristics. Systematic identification and management of DDI risks can be enhanced for both clinical trial design and patient safety in CIAS studies. By implementing a structured, evidence-based medication risk strategy, clinical teams are better equipped to anticipate and mitigate potential DDI interactions. This proactive approach supports more informed decision-making, facilitates clear communication with trial sites, and enables timely, actionable interventions, ultimately improving trial efficiency, safety and participant outcomes. These insights allow for a more precise evaluation of DDI potential in real-world clinical settings. For example, if a new molecular entity (NME) developed for CIAS is identified as an OCT2 substrate, and a participant is concurrently prescribed a known OCT2 inhibitor such as ranitidine, there may be a risk of elevated systemic exposure to the NME. In such cases, it would be advisable to switch the participant to an alternative antihistamine that does not inhibit OCT2. If a suitable alternative cannot be identified or used, the participant may need to be excluded from the trial to mitigate potential safety risks and ensure data integrity. This personalized, data-driven framework enhances patient management during trials and provides valuable guidance for clinical pharmacologists, investigators, and regulatory agencies in advancing CNS drug development with greater safety, precision, and patient-centered care.

References: 1. Rosenbrock H, Desch M, Wunderlich G. Development of the novel GlyT1 inhibitor, iclepertin (BI 425809), for the treatment of cognitive impairment associated with schizophrenia. Eur Arch Psychiatry Clin Neurosci. 2023 Oct;273(7):1557-1566. doi: 10.1007/s00406-023-01576-z. Epub 2023 Mar 27. PMID: 36971864; PMCID: PMC10465677.
2. World Health Organization. Schizophrenia. 2022 [cited 202213 July]; Available from: https://www.who.int/news-room/fact-sheets/detail/schizophrenia.
3. Gebreegziabhere Y, Habatmu K, Mihretu A, Cella M, Alem A. Cognitive impairment in people with schizophrenia: an umbrella review. Eur Arch Psychiatry Clin Neurosci. 2022 Oct;272(7):1139-1155. doi: 10.1007/s00406-022-01416-6. Epub 2022 May 28. PMID: 35633394; PMCID: PMC9508017.
4. Vita A, Barlati S, Cavallaro R, Mucci A, Riva MA, Rocca P, Rossi A, Galderisi S. Definition, assessment and treatment of cognitive impairment associated with schizophrenia: expert opinion and practical recommendations. Front Psychiatry. 2024 Sep 20;15:1451832. doi: 10.3389/fpsyt.2024.1451832. PMID: 39371908; PMCID: PMC11450451.
5. Antonio Vita, Gabriele Nibbio, Stefano Barlati, Pharmacological Treatment of Cognitive Impairment Associated With Schizophrenia: State of the Art and Future Perspectives, Schizophrenia Bulletin Open, Volume 5, Issue 1, January 2024, sgae013, https://doi.org/10.1093/schizbullopen/sgae013
6. Tang AS, Brooks L, Boudreau DM, Chanu P, Joshi A, Vora B, Zhu R. Use of Real-World Claims Data to Assess the Prevalence of Concomitant Medications to Inform Drug-Drug Interaction Risk in Target Patient Populations. Clin Pharmacol Ther. 2025 Jul;118(1):146-155. doi: 10.1002/cpt.3652. Epub 2025 Apr 1. PMID: 40170515; PMCID: PMC12166264.
7. Dołoto A, Bąk E, Batóg G, Piątkowska-Chmiel I, Herbet M. Interactions of antidepressants with concomitant medications-safety of complex therapies in multimorbidities. Pharmacol Rep. 2024 Aug;76(4):714-739. doi: 10.1007/s43440-024-00611-7. Epub 2024 Jul 16. PMID: 39012418; PMCID: PMC11294384.

Table 1: Top 10 Commonly Prescribed Comedications in CIAS Patients: Real-World Drug-Drug Interaction (DDI) Risk Assessment

Figure 1: Demographic Distribution of CIAS Patients Based on Real-World Data (2019–2024): Sex and Racial Composition