Modeling and Simulation Guides Dosing for a New Anti-psychotic Drug

Modeling and Simulation Guides Dosing for a New Anti-psychotic Drug

Drug development is becoming more complex than ever. Regulatory agencies expect sponsors to consider a wide variety of intrinsic and extrinsic factors that could impact drug safety and efficacy. These factors include intrinsic variability― CYP metabolizer status, age, sex, renal/hepatic impairment― as well as external variables― co-medications, food effects, smoker status, etc.

Clinical trials alone simply cannot evaluate all potential scenarios. Using modeling and simulation to conduct virtual trials can help sponsors optimize the dosing strategy and label claims for their drug programs. In this blog post, I’ll discuss how we used modeling and simulation to help a sponsor develop a new treatment for schizophrenia.

Improving medication adherence through a new formulation of an anti-psychotic

Despite the availability of effective treatments, schizophrenia patients frequently relapse due to poor medication adherence. Thus, Alkermes developed aripiprazole lauroxil (AL), a novel long-acting injectable (LAI) atypical anti-psychotic drug.1 Administration of AL results in extended exposure to aripiprazole, and allows for multiple dose strengths and dosing intervals, which provides flexibility for individualized patient care.

Following injection, aripiprazole lauroxil is converted to N-hydroxymethyl aripiprazole, which is then hydrolyzed to aripiprazole, the active drug. Aripiprazole is primarily eliminated by the drug metabolizing enzymes CYP2D6 and CYP3A4.

Understanding the impact of concomitant medications

AL was designed to be injected either every four (at three possible dose levels) or six weeks at the highest dose level. The drug development team at Alkermes needed to understand the impact of concomitant administration of strong CYP3A4 inhibitors and inducers and strong CYP2D6 inhibitors on AL pharmacokinetics (PK). Since CYP2D6 poor metabolizers (PMs) have a reduced ability to eliminate CYP2D6 substrates, they also wanted to know if these patients required dose adjustments.

Using PBPK models to predict the impact of concurrent medications

Physiologically-based pharmacokinetic (PBPK) models describe the behavior of drugs in the different body tissues. Depending on the route of administration, the course of the drug can be tracked through the blood and tissues. Each tissue is considered to be a physiological compartment. The concentration of the drug in each compartment is determined by combining systems data, drug data, and trial design information. The systems data includes demographic, physiological, and biochemical data for the individuals in the study population. The drug data consists of its physicochemical properties, its binding characteristics, and information on its metabolism and solubility. The trial design information comprises the dose, administration route, dosing schedule, and co-administered drugs.

The Simcyp Simulator PBPK platform was used to predict the impact of co-administration of CYP3A4 and CYP2D6 inhibitors/inducers on aripiprazole exposure in patients with varying CYP2D6 metabolizer status.2

Informing the dosing strategy

Results from the PBPK model suggested that reduction of the high and medium dose to the next lower dose in the presence of strong CYP3A4 and CYP2D6 inhibitors is needed to keep aripiprazole exposure in the target range.3 Likewise, in the presence of a strong CYP3A4 inducer, the low dose needs to be increased to the next dose level to keep aripiprazole exposure within the therapeutic window.3 The PBPK model also helped to make recommendations on dose adjustments for CYP2D6 PMs who were also taking strong CYP3A4 inhibitors.3

Aristada (injectable, extended-release aripiprazole lauroxil) received FDA approval in late 2015.4 Dose adjustments in the drug label were based on simulations that examined the effects of co-medications on aripiprazole PK. The effect of patients’ CYP2D6 genotype was also incorporated into the PBPK model and informed label claims. The insights from modeling and simulation approaches as well as the product characteristics of AL provide clinicians with flexibility in devising safe and effective treatment plans for schizophrenia patients who have difficulty with medication adherence.


References

[1] Meltzer HY, Risinger R, Nasrallah HA, et al. A randomized, double-blind, placebo-controlled trial of aripiprazole lauroxil in acute exacerbation of schizophrenia. J Clin Psychiatry. 2015;76(8):1085-1090.

[2] Hard ML, Sadler B, Mills R, Rowland Yeo K, Turncliff R, and Citrome L. Aripiprazole Lauroxil Pharmacokinetics: Application of Modeling and Simulation for Dosing Considerations of a Long-Acting Injectable Antipsychotic in Persons With Schizophrenia. Presented at the American Society of Clinical Psychopharmacology 2016 Annual Meeting. May 30-June 3, 2016, Scottsdale, AZ.

[3] ARISTADA® (aripiprazole lauroxil) extended-release injectable suspension, for intramuscular use. Prescribing information. Waltham, MA: Alkermes, Inc. October 2015. Available at: http://www.accessdata.fda.gov/drugsatfda_docs/label/2015/207533s000lbl.pdf

[4] U.S. Food and Drug Administration. FDA News Release: FDA approves new injectable drug to treat schizophrenia. 6 October 2015.


All information presented derive from public source materials.

To learn more about how PBPK modeling is used in the regulatory approvals process, please watch a webinar I gave on this topic.

Karen Rowland Yeo

About the Author

Karen Rowland Yeo

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Karen Rowland Yeo is a Vice President of PBPK consultancy services at Certara. Since 2002, Dr. Rowland Yeo has supervised a team of senior scientists who have been involved in projects relating to the extrapolation of in vitro data to predict in vivo pharmacokinetics in humans. This has included development and implementation of the models into the Simcyp Simulator which links the processes of drug discovery and development using simulations in virtual patient populations. During the past five years, she has been heavily involved in consultancy projects relating to application of the Simcyp Simulator within the pharmaceutical industry. Her specific research interests include physiologically based pharmacokinetic modeling and prediction of drug-drug interactions. Rowland Yeo has been the author/co-author of over 40 peer-reviewed articles. She received her BSc Honors degree in physics at the University of Natal in South Africa in 1989 and her PhD in drug metabolism from the University of Sheffield in 1995.