hisory of computers in pharmaceutical research presentation.pptx

history of computers m pharm

1. HISTORY OF COMPUTERS IN PHARMACEUTICAL RESEARCH & DEVELOPMENT PRESENTED BY: DHANASEKAR J, II SEMESTER M.PHARM, DEPARTMENT OF PHARMACEUTICS, NANDHA COLLEGE OF PHARMACY, ERODE . 27-10-2023 1

2. HISTORY OF COMPUTERS IN PHARMACEUTICAL RESEARCH AND DEVELOPMENT Today computers are seen everywhere in Pharmaceutical research and development that it may be hard to imagine a time when there were no computers to assist the medicinal chemist or biologist. Computers began to be utilized at pharmaceutical companies as early as the 1940s. There were several scientific and engineering advances that made possible a computational approach to design and develop a molecule. One fundamental concept understood by chemists was that chemical structure is related to molecular properties including biological activity. 27-10-2023 2

3. A quarter-century ago, the notion of a computer on the desk of every scientist and company manager was not even contemplated. Now, computers are absolutely essential for generating, managing, and transmitting information. BRIEF ACCOUNT ON HISTORIC DEVELOPMENT : In the late 1950's or early 1960'S, the first computers to have stored programs of scientific interest were acquired. One of these was an IBM 650, it had a rotating magnetic drum memory consisting of 2000 accessible registers. The programs, the data input, and the output were all in the form of IBM punched cards. 27-10-2023 3

4. EVOLUTION OF DIGITAL SYSTEM IN HEALTHCARE SYSTEM Let's review health information system trends, decade by decade, 1. 1960s : The main healthcare drivers in this era were Medicare and Medicaid The IT drivers were expensive mainframes and storage Because computers and storage were so large and expensive, hospitals typically shared a Mainframe The principal applications emerging in this environment were shared hospital accounting systems 27-10-2023 4

5. 2.1970s : One of the main healthcare drivers in this era was the need to do a better job communicating between departments (ADT, order communications, and results review) and the need for discrete departmental systems (e.g., clinical lab, pharmacy) Computers are now small enough to be installed in a single department without environmental controls As a result, departmental systems proliferated Unfortunately, these transactional systems, embedded departments, were typically islands unto themselves 27-10-2023 5

6. 3. 1980s : Healthcare drivers were heavily tied to DRG's and reimbursement For the first time, hospitals needed to pull significant information from both clinical and financial systems in order to be reimbursed At the same time, personal computers, widespread, non-traditional software applications, and networking solutions entered the market As a result, hospitals began integrating applications so financial and clinical systems could talk to each other in a limited way 27-10-2023 6

7. 4.1990s : In this decade, competition and consolidation drove healthcare, along with the need to integrate hospitals, providers, and managed care From an IT perspective, hospitals now had access to broad, distributed computing systems and robust networks Therefore, creation of Integrated delivery network (IDN)- like integration, including the impetus to integrate data and reporting 27-10-2023 7

8. 5.2000s : The main healthcare drivers were increased integration and the beginnings of outcomes-based reimbursement We now had enough technology and bedside clinical applications installed to make a serious run at commercial, real-time clinical decision support 27-10-2023 8

9. CURRENT APPLICATIONS OF COMPUTERS IN PHARMACY 1.Usage of computers in the Retail pharmacy 2. Computer aided design of drugs (CADD) 3. Use of Computers in Hospital Pharmacy 4. Data storage and retrieval 5. Information system in Pharmaceutical Industry 6. Diagnostic laboratories 7. Computer aided learning 8. Clinical trial management 9. Adverse drug events control 10. Computers in pharmaceutical formulations 11. Computers in Toxicology and Risk Assessment 12. Computational modelling of drug disposition 13. Recent development in bio computation of drug development 14. In Research Publication 15. Digital Libraries 27-10-2023 9

10. Recent software's in pharma industry Oracle JD Edwards – Manufacturing Master Control Quality Management System (QMS) NetSuite Sapphire One Fishbowl Manufacturing Lot Tracking Interface System and Business-to-Business Trading SYSPRO MAXLife365 27-10-2023 10

11. STATISTICAL MODELLING IN PHARMACEUTICAL RESEARCH AND DEVELOPMENT DESCRIPTIVE MODELS They are based on direct observation, measurement and extensive data records Descriptive model is a generic term for activities that create models by observation and experiment. Descriptive model operates on a simple logic: the maker observes a close correspondence between the behaviour of the model and that of its referent 27-10-2023 11

12. MECHANISTIC MODELS They are based on an understanding of the behavior of a system's components. A mechanistic model assumes that a complex system can be understood by examining the workings of its individual parts Mechanistic models typically have a tangible, physical aspect. In that system components are real, solid and visible. A mechanistic model is one where the basic elements of the model have a direct correspondence to the underlying mechanisms in the system being modelled 27-10-2023 12

13. Statistical Parameters Estimation (Sample Statistics) The process by which one makes inferences about a population, based on information obtained from a sample. Inferential statistics are used to determine the likelihood that a conclusion, based on the analysis of the data from a sample, is true and represents the population studied The two common forms of statistical inference are: Estimation Null hypothesis tests of significance (NHTS) 27-10-2023 13

14. Estimation A parameter is a statistical constant that describes a feature about a phenomenon, population etc. There are two forms of estimation: Point estimation (maximally likely value for parameter) Interval estimation (also called confidence interval for parameter) 27-10-2023 14

15. Point Estimation Point estimation is an estimate of a population parameter given by a single number. They are single points that estimates parameter directly which serve as a "best guess" or "best estimate" of an unknown population parameter Example: Population mean, standard deviation 27-10-2023 15

16. LIMITATION OF POINT ESTIMATION Point estimation does not provide information about sample to sample variability Point estimates are single points that are used to infer parameters directly. For example, Sample proportion pˆ(“p hat”) is the point estimator of p Sample mean x (“x bar”) is the point estimator of μ Sample standard deviation s is the point estimator of σ 27-10-2023 16

17. Confidence regions It is a set of points in an n-dimensional space, often represented as an ellipsoid around a point which is an estimated solution to a problem, although other shapes can occur Confidence regions are multivariate extensions of univariate confidence intervals 27-10-2023 17

18. Interpretation of confidence interval It provides a range of possible value for the parameter. Give information about closeness of the sample to unknown population parameter Provides a measure of the extent to which a sample estimate is likely to differ from the true population value. Indicates with a stated level of certainty, the range of values with in which the true population mean is likely to lie. CI depends on the level of confidence 27-10-2023 18

19. Null hypothesis tests of significance It is a method of statistical inference by which an experimental factor is tested against a hypothesis of no effect or no relationship based on a given observation. Here is a simple example: A school principal claims that students in her school score an average of seven out of 10 in exams. The null hypothesis is that the population mean is 7.0. Null Hypothesis It is a statement about population parameter, Denoted by H0 Tests the likelihood of the statement being true in order to decide whether to accept or reject the alternative hypothesis. It needs to be tested if it’s true. Includes signs , =,≤ or ≥ Ex: Accepted theory, ethanol boils at 73.1 degrees 27-10-2023 19

20. Test of significance It is a formal procedure for comparing observed data with a claim (also called a hypothesis) whose truth we want to assess. Test of significance is used to test a claim about an unknown population parameter. A significance test uses data to evaluate a hypothesis by comparing sample point estimates of parameters to values predicted by the hypothesis.   Null Hypothesis True False Accept if p>=0.05 (non significant) conclusion- negative 1-α (confidence level) β (type 2 error) Reject if p< 0.05 (significant) conclusion- Positive α (type 1 error) 1-β (power of the test) 27-10-2023 20

21. STATISTICAL PARAMETERS NONLINEARITY AT THE OPTIMUM It is useful to study the degree of nonlinearity of our model in a neighborhood of the forecast Briefly, there exist methods of assessing the maximum degree of intrinsic nonlinearity that the model exhibits around the optimum found. If maximum nonlinearity is excessive, for one or more parameters the confidence regions obtained applying the results of the classic theory are not to be trusted. In this case, alternative simulation procedures may be employed to provide empirical confidence regions. 27-10-2023 21

22. Sensitivity analysis It is the process by which the robustness of a cost- utility analysis (CUA) is assessed by examining the changes in the results of the analysis when key variables are varied. Sensitivity analysis is a way to predict the outcome of a decision if a situation turns out to be different compared to the key prediction   Steps to perform sensitivity analysis • Create A Model • Write A Set Of Requirements • Design A System • Make A Decision • Do A Trade off Study • Originate A Risk Analysis • Want To Discover The Cost Drivers 27-10-2023 22

23. OPTIMAL DESIGN Introduction - It is the process of finding the best way of using the existing resources while taking in to the account of all the factors that influences decisions in any experiment. The objective of designing quality formulation is achieved by various optimization techniques. In Pharmacy word “optimization” is found in the literature referring to study of the formula. 27-10-2023 23

24. Advantages of optimal designs Optimal designs reduce the costs of experimentation by allowing statistical models to be estimated with fewer experimental runs. Optimal designs can accommodate multiple types of factors, such as process and discrete factors. Designs can be optimized when the design-space is constrained, for example, when the mathematical process-space contains factor-settings that are practically infeasible ( e.g - due to safety concern)   27-10-2023 24

25. Types of optimal Design Plackett-burman designs. Factorial designs. Fractional factorial design (FFD). Response surface methodology. Central composite design (box- wilson design). Box- behnken designs 27-10-2023 25

26. STATISTICAL PARAMETERS- POPULATION MODELING Introduction Modeling and simulation have emerged as important tools for integrating data, knowledge and mechanisms to aid in arriving at rational decisions regarding drug use and development Population modeling methods provide a framework for quantitating and explaining variability in drug exposure and response Population modeling is a tool to identify and describe relationships between a subject's physiologic characteristics and observed drug exposure or response 27-10-2023 26

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28. Traditional Standard Two Stage Method It is a traditional method. It involves study of relatively small number of individuals subjected to intense sampling. The period of the study is short, since the individuals are usually categorised . Advantages It provides reliable and robust estimates when extensive numbers of samples are available for each individual. It is a simple method. It is a well tried and straightforward method to implement. Many software packages are available for this method. Disadvantages Being a controlled study design, it is very expensive and requires careful planning and implementation. It gives unreliable results in case of sparse data. 27-10-2023 28

29. Native Pooling Method It is a traditional method. In this method, the data from all individuals are pooled and analysed simultaneously without consideration of the individual from whom the specific data were obtained Advantages It may be the only viable approach in certain situations, for e.g. in case on animal data, where each animal provides only one data point. Disadvantages This method is generally considered the least favourable . It is susceptible to bias. It produces inaccurate estimates of pharmacokinetic parameters 27-10-2023 29

30. Parametric methods Mixed Effect modelling Mixed effect modelling is a parametric method which assumes a specific distribution of pharmacokinetic parameters prior to estimation. It is considered as the optimum population model method. They are of two types: Fixed Random Fixed effects are components of the structural pharmacokinetic model They do not include any unexplainable variation either between or within individuals. Fixed effect parameters are represented by the symbol theta. Random Effects: Each individual in a population will have a specific value for their pharmacokinetic parameter, which will differ from the population typical value due to unexplainable variability 27-10-2023 30

31. Non-Parametric Methods Non parametric methods do not assume any specific distribution of parameters about the population values, but rather allow for many possible distributions. In this method, the entire population distribution of each parameter is estimated from the population data. This permits visual inspection of distribution before committing to one. Different non parametric methods are: Non parametric maximum likelihood [NPML] Non parametric expectation maximization [NPEM] Nonparametric Semi/ smooth non parametric method [SNP] 27-10-2023 31

32. NON PARAMETRIC MAXIMUM LIKELIHOOD [NPML] This method permits all forms of distributions including those containing sharp changes, such as discontinuities and kinks. It uses maximum likelihood as estimator.   NON PARAMETRIC EXPECTATION MAXIMIZATION [NPEM] This method is preferred to any parametric method when there is an unexpected multimodal or non-normal distribution of at least one of the nodal parameters. It eliminates the need for initial guesses which are required for nonlinear least square procedure. It is preferable to traditional method in case of sparse data. It uses expectation maximization as the estimator. SEMI/ SMOOTH NON PARAMETRIC METHOD [SNP] This method places some restrictions on the type of parametric distributions considered. Functions that are not permitted include those containing sharp edges and discontinuities. 27-10-2023 32

33. Contents Introduction Approaches to pharmaceutical development Flow of QbD Tools applied in QbD approach ICH Q8 Pharmaceutical Development Guideline Regulatory and Industry views on QbD Conclusion 27-10-2023 33

34. introduction DEFINITION Quality by design ( QbD ) is a systematic approach to product development that begins with predefined objectives and emphasizes product and process understanding and controls based on sound science and quality risk management (ICH Q8). 27-10-2023 34

35. OBJECTIVES OF QbD The main objective of QbD is to achieve the quality products. To achieve positive performance testing. Ensures combination of product and process knowledge gained during development. From knowledgeof data process, desired attributes may be constructed. 27-10-2023 35

36. Benefits of QbD for Industry Eliminate batch failures. Minimize deviations and costly investigations. Empowerment of technical staff. Increase manufacturing efficiency, reduce costs and project rejections and waste. Better understanding of the process. Continuous improvement. Ensure better design of product with less problem. 27-10-2023 36

37. Approaches to pharmaceutical development Aspects Traditional QbD Pharmaceutical development Empirical (Experimental) Systematic and multivariate experiments. Manufacturing process Fixed Adjustable with experiment design space. Process control Offline and has wide or slow response P A T ( P r o c e s s An a l y t i c al Technique) utilized for feed back. Product specification Based on batch data Based on the desired product performance. Control strategy By end product testing Risk based, controlled shifted up stream, real time release. Life cycle management Post approval changes needed Continual improvement enable within design space. 27-10-2023 37

38. TARGET PRODUCT PROFILE(TPP) “A prospective summary of the quality characteristics of drug product that ideally will be achieved to ensure the desired quality, taking into account safety & efficacy of drug product.”(ICH Q8) Target product profile should includes- Dosage form Route of administration Dosage strength Pharmacokinetics Stability 27-10-2023 38

39. The TPP is a patient & labeling centered concepts, because it identifies the desired performance characteristics of the product, related to the patient’s need & it is organized according to the key section in the drug labeling . 27-10-2023 39

40. QUALITY TARGET PRODUCT PROFILE (QTPP) QTPP is a quantitative substitute for aspects of scientific safety & efficacy that can be used to design and optimize a formulation and manufacturing process. QTPP should only include patient relevant product performance. The Quality Target product profile is a term that is an ordinary addition of TPP for product quality. QTPP is related to identity, assay, dosage form, purity, stability in the label. 27-10-2023 40

41. CRITICAL QUALITY ATTRIBUTES(CQAS) A CQA has been defined as “a physical, chemical, biological or microbiological property or characteristics that should be within an appropriate limit, range, or distribution to ensure the desired product quality”. Critical Quality Attributes are generally associated with the drug substance, excipients , intermediates and drug product. 27-10-2023 41

42. The quality attributes of a drug product may include identity, assay, content uniformity, degradation products, residual solvents, drug release, moisture content, microbial limits. Physical attributes such as color , shape, size, odor , and friability . These attributes can be critical or not critical. 27-10-2023 42

43. CRITICAL MATERIAL ATTRIBUTES(CMA) A CMA of a drug substance, excipient , and in-process materials is a physical, chemical, biological, or microbiological characteristic of an input material that should be consistently , within an appropriate limit to ensure the desired quality of that drug substance, excipient , or in-process material. The CMA is likely to have an impact on CQA of the drug product. A material attributes can be an excipients , raw material, drug substances, reagents, solvents, packaging & labeling materials. 27-10-2023 43

44. CRITICAL PROCESS PARAMETERS(CPP) A CPP of manufacturing process are the parameters which, when changed, can potentially impact product CQA and may result in failure to meet the limit of the CQA. 27-10-2023 44 Operations during tableting CPP Wet granulation Mixing time, temperature, method of binder addition Drying Drying time, Inert air flow Milling Milling speed, screen size, feeding rate Compression Pre compression force, main compression force, dwell time, hopper design, ejection force Coating Inert air flow, time, temperature, spray pattern and rate

45. RISK ASSESSMENT Risk assessment is the linkages between material attributes & process parameters. It is performed during the lifecycle of the product to identify the critical material attributes (CMA) & critical process parameters (CPP). 27-10-2023 45

46. DESIGN SPACE This is the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality . A design space maybe built for a single unit operation or for the ensure process. 27-10-2023 46

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48. TOOLS APPLIED IN QBD APPROACH This is a systematic approach applied to conduct experiments to obtain maximum output. We have capability and expertize to perform DoE in product development using software like Minitab and Statistica . 27-10-2023 48

49. Design of experiments done by 2 methods 1. SCREENING: Designs applied to screen large number of factors in minimal number of experiments to identify the significant ones. Main purpose of these designs is to identify main effects and not the interaction effects. For such studies common designs used are 1. Placket- Burman design 2. Fractional factorial design. 27-10-2023 49

50. 2. OPTIMIZATION: Experimental designs considered to carry out optimization are mainly full factorial design, surface response methodology . e.g. Central composite Box- Behnken and Mixture designs. These designs include main effects and interactions and may also have quadratic and cubic terms require to obtain curvature. These designs are only applied once selected factors are identified, which seem to be contributing in process or formulation. 27-10-2023 50

51. RISK ASSESSMENT METHODOLOGY 1. Cause and Effect Diagrams (Fish bone/Ishikawa): This is very basic methodology to identify multiple possible factors for a single effect . Various cause associated with single effect like man, machine, material, method, system, and environment need to be considered to identify root cause. 27-10-2023 51

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53. 2. Failure mode effect analysis (FMEA): This is an important tool to evaluate potential failure modes in any process. Q ua n tif i c a t i o n o f r i s k b y i nt e r ac t ion o f p r ob a bi l ity fun c ti o n s of s e v erit y , occurrence, and detectability of any event can be done. Fmea can be effectively performed with good understanding of process. 27-10-2023 53

54. 3 PAT (Process Analytical Technology) : Assurance of product quality during intermittent steps using Process Analytical Technology (PAT) is recommended by regulatory authorities, which is yet to be extensively accepted by the pharmaceutical industry over conservative methodologies. It involves advanced online monitoring systems like NIR (Near IR), Handheld Raman Spectrometer, Online Particle Size Analyzer etc., These technologies further make assurance of continuous improvement in process and product quality through its life cycle. 27-10-2023 54

55. CONTROL STRATEGY Control strategy Based on process and product understanding, during product development sources of variability are identified. Understanding the sources of variability and their impact on processes, in-process materials, and drug product quality can enable appropriate controls to ensure consistent quality of the drug product during the product life cycle. 27-10-2023 55

56. Elements of a Control Strategy Procedural controls In-process controls Batch release testing Process monitoring Characterization testing Comparability testing Consistency testing 27-10-2023 56

57. ICH Q8(R2): PHARMACEUTICAL DEVELOPMENT GUIDELINE The ICH Q8 guideline describes GOOD PRACTICES FOR PHARMACEUTICAL PRODUCT DEVELOPMENT. ICH Q8 Pharmaceutical Development describes the principles of QbD , outlines the key elements, and provides illustrative examples for pharmaceutical drug products. It is often emphasized that the QUALITY of a pharmaceutical product should be BUILT IN BY DESIGN RATHER THAN BY TESTING ALONE. 27-10-2023 57

58. The ICH Q8 guideline suggests that those aspects of drug substances, excipients , container closure systems, and manufacturing processes that are critical to product quality, should be DETERMINED AND CONTROL STRATEGIES justified. Some of the tools that should be applied during the design space appointment include experimental designs, PAT, prior knowledge, quality risk management principles, etc. 27-10-2023 58

59. PAT(Process Analytical Technology) is a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e. during processing) of critical quality and performance attributes of raw and in process materials and processes with the goal of ensuring final product quality . Pfizer was one of the first companies to implement QbD and PAT concepts 27-10-2023 59

60. CONTENTS FOR 3.2.P.2 OF CTD QUALITY MODULE 3 Components of drug product (drug substance/ excipients ) Formulation Development. Manufacturing Process Development Container Closure System Microbiological Attributes Compatibility 27-10-2023 60

61. COMPONENTS OF DRUG PRODUCT GIVEN BY ICH Q8 DRUG SUBSTANCES- “The physicochemical and biological properties of the drug substance that can influence the performance of the drug product and its manufacturability.” Examples of physicochemical and biological properties that might need to be examined include- Solubility, Water content, Particle size, Crystal properties, Biological activity, Permeability 27-10-2023 61

62. EXCIPIENTS The excipients chosen, their concentration, and the characteristics that can influence the drug product performance or manufacturability should be discussed relative to the respective function of each excipients . Thecompatibility of the drug substance with excipients should be evaluated. For products that contain more than one drug substance, the compatibility of the drug substances with each other should also be evaluated. 27-10-2023 62

63. FORMULATION DEVELOPMENT A summary should be provided describing the development of the formulation, including identification of those attributes that are, critical to the quality of the drug product and also highlight the evolution of the formulation design from initial concept up to the final design. Information from comparative in vitro studies (e.g., dissolution) or comparative in vivo studies (e.g., BE) that links clinical formulations to the proposed commercial formulation. A successful correlation can assist in the selection of appropriate dissolution acceptance criteria, and can potentially reduce the need for further bioequivalence studies following changes to the product or its manufacturing process. 27-10-2023 63

64. CONTAINER AND CLOSURE SYSTEM The choice for selection of the container closure system for the commercial product should be discussed. The choice of materials for primary packaging and secondary packaging should be justified. A possible interaction between product and container or label should be considered. 27-10-2023 64

65. MICROBIOLOGICAL ATTRIBUTES The selection and effectiveness of preservative systems in products containing antimicrobial preservative or the antimicrobial effectiveness of products that are inherently antimicrobial. For sterile products, the integrity of the container closure system as it relates to preventing microbial contamination. Th e l o w e s t s pe cif i ed c once n t r a t ion o f a n timic r obi a l p r es e r v a t i v e s h o ul d be justified in terms of efficacy and safety, such that the minimum concentration of preservative that gives the required level of efficacy throughout the intended shelf life of the product is used. 27-10-2023 65

66. COMPATIBILITY The compatibility of the drug product with reconstitution diluents (e.g., precipitation, stability) should be addressed to provide appropriate and supportive information for the labelling. This information should cover the recommended in-use shelf life, at the recommended storage temperature and at the likely extremes of concentration. Similarly, admixture or dilution of products prior to administration (e.g., product added to large volume infusion containers) might need to be addressed. 27-10-2023 66

67. REGULATORY VIEWS ON QBD As defined by an FDA official (Woodcock, 2004 ) “The QbD concept represents product and process performance characteristics scientifically designed to meet specific objectives, not merely empirically derived from performance of test batches.” Another FDA representative (Shah, 2009) states that “introduction of the QbD concept can lead to cost savings and efficiency improvements for both industry and regulators.” 27-10-2023 67

68. QBD FACILITATES enhance opportunities for first cycle approval streamline post approval changes and regulatory processes enable more focused inspections provide opportunities for continual improvement innovation increase manufacturing efficiency reduce cost/product rejects compliance minimize/eliminate potential actions 27-10-2023 68

69. EMA, FDA, and ICH working groups have appointed the ICH quality implementation working group (Q-IWG), which prepared various templates, workshop training materials, questions and answers, as well as a points- to consider document (issued in 2011) that covers ICH Q8(R2), ICH Q9, and ICH Q10 guidelines. This document provides an interesting overview on the use of different modelling techniques in QbD 27-10-2023 69

70. There were several EMA marketing authorization applications (MAA) with QbD and PAT elements for the following products: Avamys ®, Torisel ® , Tyverb ® , Norvir ®, Exjade ® , Revolade ® , Votrient ® , etc.). Up to 2011, there was a total of 26 QbD submissions to EMA (for the new chemical entities) 18 of them were initial MAAs (4 including the real time release), 6 of them were concerning post- authorization, and 2 were scientific advice requests. An additional two MAAs were submitted for biological products, but none of the submissions were related to the generics industry. Up to 2011, there were approximately 50 QbD related applications to the FDA ( Miksinski , 2011). FDA authorities state that QbD is to be fully implemented by January 2013 ( Miksinski , 2011). 27-10-2023 70

71. INDUSTRY VIEWS ON QBD Pfizer was one of the first companies to implement QbD and PAT concepts. Through these concepts, the company gained enhanced process understanding, higher process capability, better product quality, and increased flexibility to implement continuous improvement change. 27-10-2023 71

72. QBD FOR INDUSTRY AND REGULATORY BODIES Industry Regulatory agency Development of scientific understanding of critical process and product attributes. Scientifically based assessment of product and manufacturing process design and development. Controls and testing are designed based on limits of scientific understanding at development stage. Evaluation and approval of product quality specifications in light of established standards (e.g: purity, stability, content uniformity, etc.,) Utilization of knowledge gained over the product’s lifecycle for continuous improvement. Evaluation of post-approval changes based on risk and sciences. 27-10-2023 72

73. REFERENCES History of Computers in Pharmaceutical Research and Development by ClinSkill | Dec 19, 2017 | Pharmaceutical Research (Review Article) Mannam A, Mubeen H “ Review Article Digitalisation And Automation In Pharmaceuticals From Drug Discovery To Drug Administration” international Journal of Pharmacy and Pharmaceutical Science 10(6), 2018 May 8, 1-10. Hoffmann A, IGihny-Simonius J, Marcel Plattner , Vanja Schmidli-Vckovski , Kronseder e C “Computer system validation: An overview of official requirements and standards” Pharmaceutics Acta Helvetiae , 72, 1998, 317-325. 27-10-2023 73

74. Zhang , Shirui Mao (2016 ). Application of quality by design in the current drug development: Asian journal of pharmaceutical sciences 12 (2017) 1–8 . Sushila D. Chavan , Nayana V. Pimpodkar , Amruta S. Kadam , Puja S.Gaikwad . Quality by Design : Research and Reviews: Journal of Pharmaceutical Quality Assurance|Volume 1 | Issue 2 | October- December, 2015. D . M. Patwardhan , S. S. Amrutkar , T. S. Kotwal and M. P. Wagh , APPLICATION OF QUALITY BY DESIGN TO DIFFERENT ASPECTS OF PHARMACEUTICAL TECHNOLOGIES : IJPSR, 2017; Vol. 8(9): 3649-3662. 27-10-2023 74

75. Thank you 27-10-2023 75