Computer aided formulation development

Concept of optimization optimisation in formulation development computer assisted aided

1. Computer-aided formulation development: Concept of optimization, Prepared by: Surbhi M.Pharm 2 nd sem Submitted to: Dr. Rani Mansoori

2. Need of Computer Assisted O ptimisation Techniques The Optimization of the formulation or the process is carried out by studying the influence of composition and process variables on dosage form characteristics, changing one separate/single factor at a time ( COST ), while keeping others as constant . Therefore, the conventional 'COST' approach of drug formulation development suffers from several pitfalls: • time consuming, • energy utilizing, •uneconomical , • unpredictable, • unsuitable to plug errors, • ill-suited to reveal interactions, and • yielding only workable solutions 4/4/2019 2 Apeejay Stya University

3. Benefits of Computer-based systematic design and optimization techniques • best solution in the presence of competing objectives, • fewer experiments needed to achieve an optimum formulation, • significant saving of time, effort, materials and cost, • easier problem tracing and rectification, • possibility of estimating interactions, • simulation of the product or process performance using model equation(s), and • comprehension of process to assist in formulation development and subsequent scale-up 4/4/2019 3 Apeejay Stya University

4. Such techniques are usually referred to as 'computer-aided dosage form design' (CADD). Their implementation invariably encompasses the statistical design of experiments ( DoE ), generation of mathematical equations and graphic outcomes, thus depicting a complete picture of variation of the response(s) as a function of the factor(s ). 4/4/2019 4 Apeejay Stya University

5. 1.2 OPTIMZATION: BASIC CONCEPTS AND TERMINOLOGY With respect to drug formulations or pharmaceutical processes, optimization is a phenomenon of finding "the best" possible composition or operating conditions. Optimization has been defined as the implementation of systematic approaches to achieve the best combination of product and/or process characteristics under a given set of conditions. 4/4/2019 5 Apeejay Stya University

6. OPTIMIZATION PARAMETERS 4/4/2019 Apeejay Stya University 6

7. 4/4/2019 Apeejay Stya University 7 1.PROBLEM TYPES There are two general types of optimization problems:

8. 2.Variables Independent variables : The input variables, which are directly under the control of the product development scientist, are known as independent variables . e.g ., compression force, excipient amount, mixing time, etc. 4/4/2019 8 Apeejay Stya University

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10. Factors : The independent variables, which influence the formulation characteristics or output of the process, are labeled as factors . Levels: The values assigned to the factors are termed as levels, e.g., 30°and 50°are the levels for the factor, temperature. Constraints :The restrictions placed on the factor levels are known as constraints. 4/4/2019 10 Apeejay Stya University

11. Dependent variables: The characteristics of the finished drug product or the in-process material are known as dependent variables, e.g ., drug release profile, friability, size of tablet granules, disintegration time, etc. Popularly termed as response variables . These are the measured properties of the system to estimate the outcome of the experiment. Usually these are the direct function(s) of any change(s) in the independent variables. 4/4/2019 11 Apeejay Stya University

12. Accordingly, a drug formulation (product) with respect to optimization techniques can be considered as a system, whose output (Y) is influenced by a set of controllable (X) and uncontrollable (U) input variables via a transfer function (T) 4/4/2019 12 Apeejay Stya University

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14. Response Surfaces : A response surface plot is a 3-D graphical representation of a response plotted between two independent variables and one response variable . The use of 3-D response surface plots allows understanding of the behaviour of the system by demonstrating the contribution of the independent variables. The geometric illus 4/4/2019 14 Apeejay Stya University

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16. Simultaneous Optimization Methodology Generally termed as response surface methodology ( RSM ). It is a model dependent technique. The key elements in its implementation encompass: the experimental designs , mathematical models and, the graphic outcomes . 4/4/2019 Apeejay Stya University 16

17. In this method 4/4/2019 Apeejay Stya University 17

18. Rather than estimating the effects of each variable directly, RSM involves fitting the coefficients into the model equation of a particular response variable and mapping the response , i.e., studying the response over whole of the experimental domain in the form of a surface. 4/4/2019 Apeejay Stya University 18

19. Experimental designs The designs used for simultaneous methods are frequently referred to as response surface designs . Various experimental designs frequently involved in the execution of RSM can broadly be classified as: 4/4/2019 19 Apeejay Stya University

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21. Factorial design and modifications Factorial designs (FDs; full or fractional) are the most frequently used response surface designs. These are generally based upon first-degree mathematical models. Full FDs involve studying the effect of all the factors (n) at various levels (x), including the interactions amongst them, with the total number of experiments as xn . FDs are said to be symmetric, if each factor has same number of levels, and asymmetric, if the number of levels differs for each factor Besides RSM, the design is also used for screening of influential variables and factor influence studies. 4/4/2019 21 Apeejay Stya University

22. In a full FD, as the number of factors or factor levels increases, the number of required experiments exceeds the manageable levels . Moreover, with a large number of factors, it is plausible that the highest-order interactions have no significant effect. In such cases, the number of experiments can be reduced in a systematic way, with the resulting design called as fractional factorial designs (FFD) . An FFD is a finite fraction (1/ x r ) of a complete or “ full” FD, where r is the degree of fractionation and x n -r is the total number of experiments require 4/4/2019 22 Apeejay Stya University

23. Plackett-Burman Design (PBD) is a special two-level FFD. Star designs can alleviate the problem encountered with FFDs. 4/4/2019 Apeejay Stya University 23

24. Merits : Efficient in estimating main effects and interactions Maximum usage of data Used for screening of factors, factor influence studies Limitations : Reflection of curvature not possible in a 2 level design Large number of experiments required Prediction outside the region is not advisable 4/4/2019 Apeejay Stya University 24

25. Screening The success of optimization study depends substantially upon the judicious s creening of the model . A model has to be proposed before the start of the DoE optimization study . Model selection depends upon 1.the type of the variables to be investigated and, 2. the type of the study to be made , i.e., factor screening, description of the system, or prediction of the optima or feasible regions. 4/4/2019 Apeejay Stya University 25

26. The models mostly employed to describe the response are first, second and very occasionally, third order polynomials. A first-order model is initially postulated. If a simple model is found to be inadequate for describing the phenomenon, the higher order models are followed. After hypothesizing the model, a series of computations are performed subsequently to calculate the coefficients of polynomials and their statistical significance to enable the estimation of the effects and interactions. 4/4/2019 Apeejay Stya University 26

27. 4/4/2019 Apeejay Stya University 27 Model Selection