Scholarlink

<p>Advanced <i>in silico</i> tools, such as physiologically based biopharmaceutics models (PBBM) and physiologically based pharmacokinetic models (PBPK), play a pivotal role in model-informed formulation development (MIFD). In the present study, these methodologies were utilized in the development of a novel rabeprazole modified-release (MR) formulation for improved patient compliance by reducing the dosing frequency relative to existing delayed-release (DR) formulation. A MR formulation containing combination of delayed release and pulsatile release components was designed based on the hypothetical dissolution targets. A PBBM was first established using literature-derived clinical data and used to simulate the performance of formulations with varied dissolution profiles. Initial MR formulation prototypes (once a day) were evaluated in pilot-1 bioequivalence (BE) study against reference formulation (twice a day) but results indicated infra bioequivalence outcome. After pilot-1 study, the formulation and the dissolution method were optimized and then model was refined to predict the pilot 1 outcomes. The validated model was then employed to select suitable pilot-2 prototypes and to prospectively predict their BE outcomes under fasted and fed conditions. The predicted BE ratios were in good agreement with the observed pilot-2 study results. Therefore, the same model was used to make prospective predictions with increased subjects to understand the <i>in vivo</i> performance of pivotal test formulation and lead to the successful bioequivalence outcome, product commercialization, ultimately aiding in increased patient compliance. This work highlights the value of a model-informed strategy in accelerating formulation optimization, reducing development timelines and costs, and strengthening decision-making within pharmaceutical development programs.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development of a novel rabeprazole modified release formulation to reduce the dosing frequency: A trial and triumph with physiologically based biopharmaceutics modeling

  • 期刊论文

  • 发表日期：

    2026年5月16日

查看全文
  • Harika Maddukuri,
  • Rajkumar Boddu,
  • Sangmesh Mallikarjun Chaudhari,
  • Anup Avijit Choudhury,
  • Veena Kambam,
  • Sohel Mohammed Khan,
  • Venkat Ramana Naidu,
  • Sanjay Kumar Singh,
  • Anuj Kumar Saini,
  • Sivacharan Kollipara

摘要

Advanced in silico tools, such as physiologically based biopharmaceutics models (PBBM) and physiologically based pharmacokinetic models (PBPK), play a pivotal role in model-informed formulation development (MIFD). In the present study, these methodologies were utilized in the development of a novel rabeprazole modified-release (MR) formulation for improved patient compliance by reducing the dosing frequency relative to existing delayed-release (DR) formulation. A MR formulation containing combination of delayed release and pulsatile release components was designed based on the hypothetical dissolution targets. A PBBM was first established using literature-derived clinical data and used to simulate the performance of formulations with varied dissolution profiles. Initial MR formulation prototypes (once a day) were evaluated in pilot-1 bioequivalence (BE) study against reference formulation (twice a day) but results indicated infra bioequivalence outcome. After pilot-1 study, the formulation and the dissolution method were optimized and then model was refined to predict the pilot 1 outcomes. The validated model was then employed to select suitable pilot-2 prototypes and to prospectively predict their BE outcomes under fasted and fed conditions. The predicted BE ratios were in good agreement with the observed pilot-2 study results. Therefore, the same model was used to make prospective predictions with increased subjects to understand the in vivo performance of pivotal test formulation and lead to the successful bioequivalence outcome, product commercialization, ultimately aiding in increased patient compliance. This work highlights the value of a model-informed strategy in accelerating formulation optimization, reducing development timelines and costs, and strengthening decision-making within pharmaceutical development programs.