A Brief Review on Dissolution Simulation Software

Year : 2024 | Volume : | : | Page : –
By

Mehak Jain,

Kaushiki Patel,

  1. Research Scholar,, Department of Pharmaceutics, Indore Professional Studies Academy, College of Pharmacy, Rajendra Nagar, Bijalpur, Indore,, Madhya Pradesh,, India
  2. Research Scholar,, Department of Pharmaceutics, Indore Professional Studies Academy, College of Pharmacy, Rajendra Nagar, Bijalpur, Indore,, Madhya Pradesh,, India

Abstract

‘]

Pharmaceutical industries frequently use simulation software with real-time responses for a variety of purposes, such as dissolution simulation. In essence, it is a tool that let the user to simulate an operation and see it happen without actually conducting it. Equipments are frequently designed using simulation software to ensure that the finished output will be as near as to the design specifications as possible without costly process adjustments. Advanced computer programs can simulate power system behavior, complex biological process. These are employed to simulate physical responses in real time. A set of mathematical formulas are used to simulate real-world phenomena in simulation software. Simulator software is frequently used in equipment design to get the finished product as near to the design parameters as feasible without having to make expensive process adjustments. The purpose of simulation software is to examine the feasibility and reproducibility of developing an in vitro/in vivo interaction using GastroPLUSTM, PK- Sim®, Simcyp®, and DDDPlus (IVIVR). These findings emphasize the significance of choosing a suitable experimental strategy to determine the in vitro dissolution rate, which will later be used as a basis to create in vitro- in vivo correlations.

Keywords: Dissolution Simulation software, vitro-in vivo relationship (IVIVR), GastroPLUS™, PK-Sim®, Simcyp®, DDDPlus.

How to cite this article:
Mehak Jain, Kaushiki Patel. A Brief Review on Dissolution Simulation Software. Research & Reviews: A Journal of Pharmaceutical Science. 2024; ():-.
How to cite this URL:
Mehak Jain, Kaushiki Patel. A Brief Review on Dissolution Simulation Software. Research & Reviews: A Journal of Pharmaceutical Science. 2024; ():-. Available from: https://journals.stmjournals.com/rrjops/article=2024/view=170709



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References ‘]

1) Mahmud, K, Soetanto, D., & Town, G. E. (2018). Energy management softwares and tools. In I. Dincer (Ed.), Comprehensive energy systems: energy management (Vol. 5, pp. 202-257). Elsevier.
2) Al-Tabakha MM, Alomar MJ. In vitro dissolution and in silico modeling shortcuts in bioequivalence testing. Pharmaceutics. 2020 Jan 4;12(1):45. https://doi.org/10.3390/pharmaceutics12010045
3) Otsuka K, Shono Y, Dressman J. Coupling biorelevant dissolution methods with physiologically based pharmacokinetic modelling to forecast in-vivo performance of solid oral dosage forms. Journal of Pharmacy and Pharmacology. 2013 Jul;65(7):937-52.
4) Chen Y, Jiao T, McCall TW, Baichwal AR, Meyer MC. Comparison of four artificial neural network software programs used to predict the in vitro dissolution of controlled-release tablets. Pharmaceutical development and technology. 2002 Jan 1;7(3):373-9.
5) Minekus M, Marteau P, Havenaar R, Veld JH. A multicompartmental dynamic computer- controlled model simulating the stomach and small intestine. Alternatives to laboratory animals. 1995 Mar;23(2):197-209.
6) Vertzoni M, Dressman J, Butler J, Hempenstall J, Reppas C. Simulation of fasting gastric conditions and its importance for the in vivo dissolution of lipophilic compounds. European Journal of Pharmaceutics and Biopharmaceutics. 2005 Aug 1;60(3):413-7.
7) Jantratid E, Janssen N, Reppas C, Dressman JB. Dissolution media simulating conditions in the proximal human gastrointestinal tract: an update. Pharmaceutical research. 2008 Jul;25(7):1663-76.
8) Shono Y, Jantratid E, Janssen N, Kesisoglou F, Mao Y, Vertzoni M, Reppas C, Dressman JB. Prediction of food effects on the absorption of celecoxib based on biorelevant dissolution testing coupled with physiologically based pharmacokinetic modeling. European Journal of Pharmaceutics and Biopharmaceutics. 2009 Sep 1;73(1):107-14.

9) Thelen K, Coboeken K, Willmann S, Burghaus R, Dressman JB, Lippert J. Evolution of a detailed physiological model to simulate the gastrointestinal transit and absorption process in humans, part 1: oral solutions. Journal of pharmaceutical sciences. 2011 Dec 1;100(12):5324-45.
10) Agoram B, Woltosz WS, Bolger MB. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Advanced drug delivery reviews. 2001 Oct 1;50: S41-67.
11) Hack CE, Efremenko AY, Pendse SN, Ellison CA, Najjar A, Hewitt N, Schepky A, Clewell III HJ. Physiologically based pharmacokinetic modeling software. In Physiologically Based Pharmacokinetic (PBPK) Modeling 2020 Jan 1 (pp. 81-126). Academic Press.
12) Kuentz M, Nick S, Parrott N, Rothlisberger D. A strategy for preclinical formulation development using GastroPlus™ as pharmacokinetic simulation tool and a statistical screening design applied to a dog study. European journal of pharmaceutical sciences. 2006 Jan 1;27(1):91-9.
13) Wei H, Dalton C, Di Maso M, Kanfer I, Löbenberg R. Physicochemical characterization of five glyburide powders: a BCS based approach to predict oral absorption. European Journal of pharmaceutics and Biopharmaceutics. 2008 Aug 1;69(3):1046-56.
14) Okumu A, DiMaso M, Löbenberg R. Dynamic dissolution testing to establish in vitro/in vivo correlations for montelukast sodium, a poorly soluble drug. Pharmaceutical research. 2008 Dec;25(12):2778-85.
15) Tubic-Grozdanis M, Bolger MB, Langguth P. Application of gastrointestinal simulation for extensions for biowaivers of highly permeable compounds. The AAPS journal. 2008 Mar;10(1):213-26.
16) Huang W, Lee SL, Yu LX. Mechanistic approaches to predicting oral drug absorption. The AAPS journal. 2009 Jun;11(2):217-24.

17) Havenaar R, Anneveld B, Hanff LM, de Wildt SN, de Koning BA, Mooij MG, Lelieveld JP, Minekus M. In vitro gastrointestinal model (TIM) with predictive power, even for infants and children. International journal of pharmaceutics. 2013 Nov 30;457(1):327-32.
18) Minekus M, Marteau P, Havenaar R, Veld JH. A multicompartmental dynamic computer- controlled model simulating the stomach and small intestine. Alternatives to laboratory animals. 1995 Mar;23(2):197-209.
19) Blanquet S, Zeijdner E, Beyssac E, Meunier JP, Denis S, Havenaar R, Alric M. A dynamic artificial gastrointestinal system for studying the behavior of orally administered drug dosage forms under various physiological conditions. Pharmaceutical research. 2004 Apr;21(4):585-91.
20) Elashoff JD, Reedy TJ, Meyer JH. Analysis of gastric emptying data. Gastroenterology.1982 Dec 1;83(6):1306-12.

21) Minekus M. Development and validation of a dynamic model of the gastrointestinal tract.The Netherlands: University of Utrecht; 1998 May 28.
22) Bellmann S, Minekus M, Zeijdner E, Verwei M, Sanders P, Basten W, Havenaar R. TIM- carbo: a rapid, cost-efficient and reliable in vitro method for glycaemic response after carbohydrate ingestion. Dietary fibre: new frontiers for food and health. Wageningen Academic Publishers, Wageningen. 2010 Apr 21:467-73.
23) Yu A, Sun D, Li BV, Yu LX. Bioequivalence history. In FDA Bioequivalence standards 2014 (pp. 1-27). Springer, New York, NY.

24) Poulin P, Theil FP. A priori prediction of tissue: plasma partition coefficients of drugs to facilitate the use of physiologically‐based pharmacokinetic models in drug discovery. Journal of pharmaceutical sciences. 2000 Jan 1;89(1):16-35.
25) Krishnan K, Crouse LC, Bazar MA, Major MA, Reddy G. Physiologically based pharmacokinetic modeling in toxicology. In Principles and Methods of Toxicology, Hayes AW 2001. 1994;5.
26) Jamei M, Marciniak S, Feng K, Barnett A, Tucker G, Rostami-Hodjegan A. The Simcyp® population-based ADME simulator. Expert opinion on drug metabolism & toxicology. 2009 Feb 1;5(2):211-23.
27) Fan J, Zhang X, Zhao L. Utility of physiologically based pharmacokinetic absorption modeling to predict the impact of salt-to-base conversion on prasugrel HCl product bioequivalence in the presence of proton pump inhibitors. The AAPS Journal. 2017 Sep;19(5):1479-86.
28) Mager DE, Jusko WJ. General pharmacokinetic model for drugs exhibiting target-mediated drug disposition. Journal of pharmacokinetics and pharmacodynamics. 2001 Dec;28(6):507-32.
29) Certara SimcypTM PBPK Simulator. Predicting Drug Performance. (2022, October 13) Certara. Retrieved October 18, 2022 from https://www.certara.com/software/simcyp-pbpk/
30) Jamei M, Yang J, Turner D, Yeo KR, Tucker GT, Rostami-Hodjegan A. A novel physiologically-based mechanistic model for predicting oral drug absorption: the advanced dissolution, absorption, and metabolism (ADAM) model. InThe 4th World Conference on Drug Absorption, Transport and Delivery 2007 Jun 20.
31) Polak S, Jamei M, Turner DB, Yang J, Neuhoff S, Tucker GT, Rostami-Hodjegan A. Prediction of the in vivo behaviour of modified release formulations of metoprolol from in vitro dissolution profiles using the ADAM model (Simcyp® V8).
32) Dokoumetzidis A, Kalantzi L, Fotaki N. Predictive models for oral drug absorption: from in silico methods to integrated dynamical models. Expert opinion on drug metabolism & toxicology. 2007 Aug 1;3(4):491-505.
33) Almukainzi M, Okumu A, Wei H, Löbenberg R. Simulation of in vitro dissolution behavior using DDDPlus™. AAPS Pharm SciTech. 2015 Feb;16(1):217-21.

34) Dickinson PA, Lee WW, Stott PW, Townsend AI, Smart JP, Ghahramani P, Hammett T, Billett L, Behn S, Gibb RC, Abrahamsson B. Clinical relevance of dissolution testing in quality by design. The AAPS journal. 2008 Jun;10(2):380-90.
35) Gray V, Kelly G, Xia M, Butler C, Thomas S, Mayock S. The science of USP 1 and 2 dissolution: present challenges and future relevance. Pharmaceutical research. 2009 Jun;26(6):1289-302.
36) Smith FP, Holzworth DP, Robertson MJ. Linking icon-based models to code-based models: a case study with the agricultural production systems simulator. Agricultural Systems. 2005 Feb 1;83(2):135-51.
37) Eedara BB, Tucker IG, Das SC. A STELLA simulation model for in vitro dissolution testing of respirable size particles. Scientific reports. 2019 Dec 6;9(1):1-4.
38) Mikulecky DC. Modeling intestinal absorption and other nutrition-related processes using PSPICE and STELLA. Journal of Paediatric Gastroenterology and Nutrition. 1990 Jul 1;11(1):7-20.
39) )Podczeck F. Comparison of in vitro dissolution profiles by calculating mean dissolution time (MDT) or mean residence time (MRT). International journal of pharmaceutics. 1993 Aug 15;97(1-3):93-100.
40) Mendyk A, Jachowicz R, Fijorek K, Dorożyński P, Kulinowski P, Polak S. KinetDS: an open-source software for dissolution test data analysis. Dissolution Technol. 2012 Feb 1;19(1):6-11.
41) Kulinowski P, Hudy W, Mendyk A, Juszczyk E, Węglarz WP, Jachowicz R, Dorożyński P. The relationship between the evolution of an internal structure and drug dissolution from controlled-release matrix tablets. Aaps Pharmscitech. 2016 Jun;17(3):735-42.

 


Ahead of Print Subscription Review Article
Volume
Received July 23, 2024
Accepted August 27, 2024
Published September 7, 2024

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