Asbjørn Nøhr-Nielsen (1), Marie Louise De Bruin (1), Mikael Thomsen (2), Christian Bressen Pipper (1), Ole Jannik Bjerrum (1), Trine Meldgaard Lund (1)
(1) University of Copenhagen, (2) Contera Pharma
Introduction: Fixed dose-combination (FDC) products, a subset of combination therapies, contain a fixed ratio of two or more active ingredients with distinct modes of action formulated into a single dosage form. FDC products contribute to treatment regimens with unique advantages compared to conventional monotherapies by providing an enhanced clinical efficacy and safety profile, improved patient compliance and convenience, and opportunities for development of novel treatment entities through synergistic action of the components1–3. From a regulatory perspective, FDC products are subject to separate regulations by the US Food and Drug Administration (FDA), European Medicines Agency’s (EMA), and the WHO4–6. The EMA guidelines specify that it is required by the applicant to demonstrate that each active substance contributes to efficacy and/or benefit-risk balance. To achieve this it is suggested to perform a factorial design study – at least when pursuing the add-on or initial treatment indications outlined in the Guideline on clinical development of fixed combination medicinal products6. However, it could be questioned whether a consequence of these comprehensive recommendations for FDC products is that excessive investigations with factorial design are being performed7.
Objectives:
- Assess the body of evidence submitted with a Marketing Authorization Applications (MAAs), with a specific focus on the characteristics and indications of the FDC products as well as the number and size of clinical trials conducted.
- Examine approaches applied during the clinical development, specifically the dose selection process, clinical trial design, and the use of pharmacokinetic-pharmacodynamics modelling.
Methods: The main resource for this study was the European public assessment reports (EPARs), which are published by the EMA and are publicly available online8. The present study included applications with authorization date from January 2010 to December 2016 and considered only the applications in which the drug product contained two active substances. Neither products that were refused approval by the CHMP nor products that were withdrawn after market authorization were considered. Additionally, products that were considered either generic, biosimilar, vaccine or orphan products were considered to be outside the scope of this review and were therefore excluded. Statistical analyses were performed using R 3.4.3 for windows9, supplemented with the R package “geepack” for generalized estimation equations10. A full model approach was used, containing four separate response variables (trials, arms, patients, and doses tested) and six predictor variables (ATC code/design, clinical trial phase, FDC type, PK-PD modelling and number of approved FDC doses).
Results: The bulk of clinical evidence for FDC products is found in phase III as many sponsors seem to put less emphasis on phase II trials. For FDC type there was a clear increase in trials, arms and patients for two approved drugs compared to those that included one or two new molecular entities, however, this increase was not seen for doses tested. More than half of FDC products composed of two previously approved components were approved without a dose finding trial. Certain types of PK-PD modelling had a significant effect on the number of doses studied. Lastly, for clinical trial design we found that significantly more patients are included for factorial design compared to ray design.
Conclusion: These finding suggest that a factorial design study should be carefully considered as a ray design could provide similar information using far less patients. Furthermore, sponsors could consider greater emphasis on phase II and employing the use of PK-PD modelling in order to have a greater pool of information available when designing the larger phase III studies, thereby grouping patients on fewer combination doses, increasing the likelihood of showing superiority of the combination over the individual components. This is especially true for combinations of two previously approved drugs, as the data showed equally many combination doses tested for these FDC products as those containing one or two new molecular entities.
References:
[1] Kwon, K. C. & Lee, C. Analysis of Fixed-Dose Combination Products Approved by the US Food and Drug Administration, 2010-2015. Ther. Innov. Regul. Sci. 51, 111–117 (2017).
[2] Moon, C. & Oh, E. Rationale and strategies for formulation development of oral fixed dose combination drug products. J. Pharm. Investig. 46, 615–631 (2016).
[3] FIXED-DOSE COMBINATIONS – Fixed-Dose Combination Products – A Review (Part 1 – Introduction) | Articles | drug development and delivery back issues | Drug Development & Delivery. Available at: http://www.drug-dev.com/Main/Back-Issues/FIXEDDOSE-COMBINATIONS-FixedDose-Combination-Produ-661.aspx. (Accessed: 5th April 2017)
[4] World Health Organization. Guidelines for registration of fixed-dose combination medicinal products. WHO Tech. Rep. Ser. No. 929 (2005).
[5] FDA/CDER. Guidance for Industry- Fixed Dose Combinations , Antiretrovirals for the Treatment of HIV Guidance for Industry Fixed Dose Combinations , Previously Approved Antiretrovirals for the Treatment of HIV. Food Drug Adm. Rockville, MD 1–36 (2006).
[6] Medicines Agency, E. Guideline on clinical development of fixed combination_ public consultation. (2015).
[7] Forda, S. R., Bergström, R., Chlebus, M., Barker, R. & Andersen, P. H. Priorities for improving drug research, development and regulation. Nat. Rev. Drug Discov. 12, 247–248 (2013).
[8] European Medicines Agency – Find medicine – European public assessment reports. Available at: http://www.ema.europa.eu/ema/index.jsp?curl=pages/medicines/landing/epar_search.jsp&mid=WC0b01ac058001d124.
[9] R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2016).
[10] Halekoh, U., Højsgaard, S. & Yan, J. The R Package geepack for generalized estimating equations. J. Stat. Softw. 15, 1–11 (2006).
Reference: PAGE 27 (2018) Abstr 8628 [www.page-meeting.org/?abstract=8628]
Poster: Methodology - Other topics