Unit-I

Chapter 1: Computers in Pharmaceutical Research and Development

1.1.

Computer Aided Drug Development

15

1.1.1.

Introduction

15

1.1.2.

Objectives of Computer-Aided Drug Development

15

1.1.3.

Components of Computer-Aided Drug Development

15

1.1.4.

Applications of Computer-Aided Drug Development

16

1.2.

Computers in Pharmaceutical Research and Development

17

1.2.1.

General Overview

17

1.2.2.

History of Computers in Pharmaceutical Research and Development

18

1.2.3.

Applications of Computers in Pharmaceutical Research  and Development

21

1.3.

Statistical Modeling in Pharmaceutical Research and Development

24

1.3.1.

Introduction

24

1.3.2.

History of Statistical Modeling

25

1.3.3.

Objectives of Statistical Modeling

25

1.3.4.

Types of Statistical Modeling

26

1.3.5.

Significance of Statistical Modeling

28

1.3.6.

Applications of Statistical Modeling

28

1.4.

Descriptive versus Mechanistic Modeling

29

1.4.1.

Introduction

29

1.4.2.

Descriptive or Empirical Modeling

29

1.4.3.

Mechanistic Modeling

30

1.5.

Statistical Parameters

32

1.5.1.

Introduction

32

1.5.2.

Types of Statistical Parameters

32

1.5.3.

Data Types in Statistics

34

1.5.3.1.

Qualitative Data (Categorical Data)

34

1.5.3.2.

Quantitative Data (Numerical Data)

35

1.5.4.

Applications of Descriptive Statistics in Pharmaceutical Research and Development

35

1.5.5.

Statistical Parameter Estimation

36

1.5.5.1.

Point Estimation

36

1.5.5.2.

Interval Estimation

36

1.5.6.

Confidence Regions

37

1.5.7.

Non-Linearity at the Optimum

41

1.6.

Sensitivity Analysis

43

1.6.1.

Introduction

43

1.6.2.

Sensitivity Analysis Methods

44

1.7.

Optimal Design

45

1.8.

Population Modeling

47

1.8.1.

Introduction

47

1.8.2.

Rationale for Population Modeling

48

1.8.3.

Population PK/PD Models

48

1.8.4.

Components of Population Models

49

1.8.5.

Benefits of Population Modeling

50

1.8.6.

Challenges and Consideration of Population Modeling

50

1.8.7.

Population versus Non-Population Modeling

50

1.9.

Exercise

51

Chapter 2: Quality by Design in Pharmaceutical Development

2.1.

Quality-by-Design (QbD) in Pharmaceutical Development

54

2.1.1.

Introduction

54

2.1.2.

Principles of QbD

54

2.1.3.

Objectives of QbD

55

2.1.4.

Benefits of QbD in Industry and Regulation Bodies

55

2.1.5.

Scope of QbD

56

2.1.6.

Elements of QbD

57

2.1.6.1.

Quality Target Product Profile (QTPP)

57

2.1.6.2.

Critical Quality Attributes (CQAs)

58

2.1.6.3.

Quality Risk Management (QRM)

59

2.1.6.4.

Design Space

60

2.1.6.5.

Control Strategy

61

2.1.6.6.

Life Cycle Management

62

2.1.6.7.

Critical Material Attributes (CMAs)

63

2.1.6.8.

Critical Process Parameters (CPPs)

63

2.1.6.9.

Design of Experiments (DoE)

65

2.1.6.10.

Process Analytical Technology (PAT)

65

2.1.7.

Example of Developing a Pharmaceutical Formulation using QbD

65

2.1.8.

Advantages of QbD

66

2.1.9.

Disadvantages of QbD

66

2.1.10.

Applications of QbD

67

2.1.11.

Comparison between Traditional versus QbD Approach

68

2.2.

ICH Q8 Guideline

69

2.2.1.

Introduction

69

2.2.2.

Objectives of ICH Q8 Guideline

69

2.2.3.

Scope of ICH Q8 Guideline

70

2.2.4.

Pharmaceutical Development

70

2.2.4.1.

Components of the Drug Product (3.2.P.2.1)

71

2.2.4.2.

Drug Product

72

2.2.4.3.

Manufacturing Process Development

73

2.2.4.4.

Container Closure System

74

2.2.4.5.

Microbiological Attributes

74

2.2.4.6.

Compatibility

75

2.2.5.

Role of ICH Q8 Guideline

75

2.3.

ICH Q9 Guideline

76

2.4.

ICH Q10 Guideline

77

2.5.

Regulatory and Industry Views on QbD

78

2.5.1.

Regulatory Views on QbD

78

2.5.2.

Industry Views on QbD

81

2.6.

Scientifically Based QbD

82

2.6.1.

Introduction

82

2.6.2.

Examples of Application of Scientifically-Based QbD

83

2.7.

Exercise

84

Unit-II

Chapter 3: Computational Modeling of Drug Disposition

3.1.

Computational Modeling of Drug Disposition

87

3.1.1.

Introduction

87

3.1.2.

Need for Computational Modeling in Drug Disposition

88

3.1.3.

Computational Modeling of ADMET

88

3.1.4.

Modeling Techniques

90

3.1.4.1.

Quantitative Modeling

90

3.1.4.2.

Qualitative Modeling

91

3.1.4.3.

Other Significant Modeling Techniques

92

3.1.5.

Applications of Computational Modeling in Drug Development

93

3.2.

Drug Absorption

93

3.2.1.

Introduction

93

3.2.2.

Parameters of Drug Absorption

93

3.2.3.

Computational Modeling Approaches for Drug Absorption

94

3.2.4.

Importance of Modeling Drug Absorption

96

3.3.

Solubility

96

3.3.1.

Introduction

96

3.3.2.

Computational Modeling Approaches for Solubility

96

3.3.3.

Factors Influencing Solubility Modeling

98

3.4.

Intestinal Permeation

98

3.4.1.

Introduction

98

3.4.2.

Computational Modeling Approaches for Intestinal Permeation

98

3.5.

Drug Distribution

100

3.5.1.

Introduction

100

3.5.2.

Volume of Distribution (Vd)

100

3.5.3.

Plasma-Protein Binding (PPB)

100

3.5.4.

Blood-Brain Barrier (BBB) Permeability

100

3.5.5.

Computational Modeling Approaches for Drug Distribution

101

3.5.6.

Factors Influencing Distribution Modeling

102

3.6.

Drug Excretion

102

3.6.1.

Introduction

102

3.6.2.

Computational Modeling Approaches for Drug Excretion

102

3.6.3.

Factors Influencing Excretion Modeling

104

3.7.

Active Transport

104

3.7.1.

Introduction

104

3.7.2.

Transporters

105

3.7.3.

Active Transporters

105

3.7.3.1.

Influx Transporter

106

3.7.3.2.

Efflux Transporter

107

3.8.

P-gp Efflux Transporter

108

3.8.1.

Introduction

108

3.8.2.

Computational Modeling Approaches for P-gp Efflux Transporter

108

3.8.3.

Inhibition of P-gp Efflux Transporter

109

3.8.4.

Role of P-gp Efflux Transporter in Drug Disposition

109

3.8.5.

Role of P-gp Efflux Transporter in Cell Homeostasis and Bioavailability

110

3.8.6.

Role of P-gp Inhibition for Optimal Drug Delivery

110

3.9.

BCRP (Breast Cancer Resistance Protein) Efflux Transporter

111

3.9.1.

Introduction

111

3.9.2.

Computational Modeling Approaches for BCRP Efflux Transporter

111

3.9.3.

Role of BCRP Efflux Transporter in Drug Disposition

112

3.10.

Nucleoside Transporters

112

3.10.1.

Introduction

112

3.10.2.

Types of Nucleoside Transporters

112

3.10.3.

Computational Modeling Approaches for Nucleoside Transporters

113

3.10.4.

Role of Nucleoside Transporters in Drug Disposition

113

3.11.

hPEPT1 (Human Peptide Transporter 1)

113

3.11.1.

Introduction

113

3.11.2.

Computational Modeling Approaches for hPEPT1

114

3.11.3.

Role of hPEPT1 in Drug Disposition

114

3.12.

ASBT (Apical Sodium-Dependent Bile Acid Transporter)

114

3.12.1.

Introduction

114

3.12.2.

Computational Modeling Approaches for ASBT

114

3.12.3.

Role of ASBT in Drug Disposition

115

3.13.

OCT (Organic Cation Transporters)

115

3.13.1.

Introduction

115

3.13.2.

Computational Modeling Approaches for OCT

115

3.13.3.

Role of OCT in Drug Disposition

116

3.14.

OATP (Organic Anion Transporting Polypeptides)

116

3.14.1.

Introduction

116

3.14.2.

Computational Modeling Approaches for OATP

117

3.14.3.

Role of OATP in Drug Disposition

117

3.15.

BBB-Choline Transporter

117

3.15.1.

Introduction

117

3.15.2.

Computational Modeling Approaches for BBB-Choline Transporter

118

3.15.3.

Role of BBB-Choline Transporter in Drug Disposition

118

3.16.

Exercise

118

Unit-III

Chapter 4: Computer-Aided Formulation Development

4.1.

Computer-Aided Formulation Development

122

4.2.

Concept of Optimization

123

4.2.1.

Introduction

123

4.2.2.

Terms used in Optimisation

124

4.2.3.

Computer Software Used in Optimisation

128

4.2.4.

Objectives/Need of Optimisation

128

4.2.5.

Benefits of Optimisation

128

4.2.6.

Process of Optimisation

129

4.2.7.

Optimization Parameters

130

4.2.8.

Importance of Optimisation in Pharmaceutical Formulation

132

4.3.

Experimental Design/Design of Experiment (DoE)

133

4.3.1.

Introduction

133

4.3.2.

Importance of Experimental Design

133

4.3.3.

Types of Experimental Design

133

4.3.4.

Keys Steps for Experimental Design

135

4.3.5.

Applications of Experimental Design in Pharmaceutical Formulation

136

4.4.

Optimisation Technology

137

4.4.1.

Introduction

137

4.4.2.

Evolutionary Operations

137

4.4.3.

Simplex Method

138

4.4.4.

Lagrangian Method

138

4.4.5.

Search Method

141

4.4.6.

Canonical Analysis

143

4.5.

Screening Design

143

4.5.1.

Introduction

143

4.5.2.

Objectives of Screening Design

144

4.5.3.

Types of Screening Design

144

4.5.4.

Importance of Screening Design

145

4.6.

Factorial Design

145

4.6.1.

Introduction

145

4.6.2.

Key Features of Factorial Design

146

4.6.3.

Types of Factorial Design

146

4.6.3.1.

Full Factorial Design

146

4.6.3.2.

Fractional Factorial Design

148

4.6.4.

Steps Involved in Using Factorial Design

150

4.6.5.

Advantages of Factorial Design

151

4.6.6.

Disadvantages of Factorial Design

151

4.6.7.

Applications of Factorial Design in Formulation

151

4.7.

Response Surface Method (RSM)

152

4.7.1.

Introduction

152

4.7.2.

Objectives of RSM

152

4.7.3.

Key Components of RSM

153

4.7.4.

Steps in Implementing RSM in Pharmaceutical Formulation

154

4.7.5.

Advantages of RSM

155

4.7.6.

Limitations of RSM

155

4.7.7.

Applications of RSM

155

4.8.

Exercise

156

Chapter 5: Computers in Pharmaceutical Formulation

5.1.

Computers in Pharmaceutical Formulation

159

5.1.1.

Introduction

159

5.1.2.

Role of Computer in Pharmaceutical Formulation

159

5.2.

Development of Pharmaceutical Emulsions

160

5.2.1.

Introduction

160

5.2.2.

Applications of Computer-Aided Techniques in Development of Pharmaceutical Emulsions

161

5.2.3.

Steps in Computer-Aided Development of Pharmaceutical Emulsions

162

5.2.4.

Factorial Design in Development of Emulsion

164

5.3.

Development of Microemulsion Drug Carriers

166

5.3.1.

Introduction

166

5.3.2.

Significance of Computers in the Development of Pharmaceutical Microemulsions

166

5.3.3.

Strategies for Formulation of Microemulsions

167

5.3.4.

Optimisation Designs in the Preparation of Microemulsion

168

5.4.

Legal Protection of Innovative Uses of Computers in R&D

171

5.4.1.

Introduction

171

5.4.2.

Intellectual Property Rights

171

5.4.2.1.

Patents

173

5.4.2.2.

Copyright

174

5.4.2.3.

Database Rights

175

5.4.2.4.

Trade Secret

175

5.4.2.5.

Trademark

176

5.4.3.

Significance of Legal Protection for Computers in Pharmaceutical R&D

177

5.5.

The Ethics of Computing in Pharmaceutical Research

177

5.5.1.

Introduction

177

5.5.2.

Ethical Issues

178

5.5.3.

Codes of Conduct Relevant to the Use of Computer

180

5.6.

Computers in Market Analysis

181

5.6.1.

Introduction

181

5.6.2.

Use of Computers in Market Analysis

181

5.6.3.

Advantages of Computers in Market Analysis

182

5.7.

Exercise

182

Unit-IV

Chapter 6: Computer-Aided Biopharmaceutical Characterization

6.1.

Computer-Aided Biopharmaceutical Characterization

185

6.2.

Gastrointestinal Absorption Simulation

187

6.2.1.

Introduction

187

6.2.2.

Computational Models for Gastrointestinal Absorption

187

6.2.3.

Mathematical Models for Gastrointestinal Absorption

188

6.2.4.

Parameters for Computer-Aided Gastrointestinal Absorption Simulation

188

6.2.5.

Theoretical Background of Gastrointestinal Absorption Simulation

189

6.2.6.

Importance of Computer-Aided Biopharmaceutical Characterisation for Gastrointestinal Absorption Simulation

192

6.2.7.

Model Construction

193

6.2.7.1.

Process of Model Construction

193

6.2.7.2.

Example of Model Construction

194

6.3.

Parameter Sensitivity Analysis

198

6.3.1.

Introduction

198

6.3.2.

Methods for Parameter Sensitivity Analysis

198

6.3.3.

Example of Parameter Sensitivity Analysis

199

6.3.4.

Importance of Parameter Sensitivity Analysis

200

6.4.

Virtual Trial

201

6.4.1.

Introduction

201

6.4.2.

Example of Virtual Trial

201

6.4.3.

Importance of Virtual Trial

203

6.5.

Fed vs. Fasted State

204

6.5.1.

Introduction

204

6.5.2.

Impact of Fed vs. Fasted State

204

6.5.3.

Gastrointestinal Absorption Simulation for Fed vs. Fasted State

206

6.5.4.

Differences Between Fed vs. Fasted State

206

6.6.

In Vitro Dissolution and In Vitro-In Vivo Correlation

207

6.6.1.

Introduction

207

6.6.2.

In Vitro Dissolution

207

6.6.3.

In Vitro-In Vivo Correlation

208

6.6.3.1.

Purpose of IVIVC

208

6.6.3.2.

Levels of IVIVC

209

6.6.3.3.

Methods of IVIVC (Convolution and Deconvolution)

211

6.6.3.4.

Significance of IVIVC in Biopharmaceutical Characterisation and Pharmaceutical Drug Development

213

6.7.

Biowaiver Considerations

214

6.7.1.

Introduction

214

6.7.2.

BCS-based Biowaivers and its Requirements

215

6.7.3.

Role of Computer Aided Technology Simulation to Justify Biowaiver

216

6.7.4.

Exception for Biowaiver Consideration

217

6.8.

Exercise

217

Chapter 7: Computer Simulations in Pharmacokinetics and Pharmacodynamics

7.1.

Computer Simulations in Pharmacokinetics and Pharmacodynamics

220

7.1.1.

Introduction

220

7.1.2.

Pharmacokinetics and Pharmacodynamics Simulation

221

7.1.3.

Importance of Computer Simulation in Pharmacokinetics and Pharmacodynamics

222

7.2.

Levels of Computer Simulation

223

7.2.1.

Introduction

223

7.2.2.

Level 1 - Computer Simulation of Whole Organism

223

7.2.2.1.

Lumped-Parameter PK-PD Model

223

7.2.2.2.

Physiological Modeling

224

7.2.3.

Level 2 - Computer Simulation of Isolated Tissues and Organs

225

7.2.4.

Level 3 - Computer Simulation of Cell

227

7.2.5.

Level 4 - Computer Simulation of Proteins and Genes

228

7.3.

Exercise

229

Chapter 8: Computers in Clinical Development

8.1.

Computers in Clinical Development

231

8.1.1.

Introduction

231

8.1.2.

Significance/Role of Computers in Clinical Data Development

232

8.2.

Clinical Data Collection and Management

233

8.2.1.

Introduction

233

8.2.2.

Data Collection versus Management

233

8.2.3.

Communication in Clinical Data Collection and Management

234

8.2.4.

Methods of Clinical Data Collection

235

8.2.4.1.

Pure Paper-Based Systems

235

8.2.4.2.

Pure Electronic-Based Systems

236

8.2.4.3.

Hybrid Paper-Based Systems

238

8.2.5.

Acquiring Proprietary e-Clinical Software

239

8.2.6.

Integration of Communication

240

8.2.6.1.

Process Before Data Collection

240

8.2.6.2.

Process during Data Collection

241

8.2.6.3.

Process after Data Collection

242

8.2.7.

Clinical Data Management

243

8.2.7.1.

Process of Clinical Data Management

243

8.2.7.2.

Regulation of Clinical Data Management

244

8.2.8.

Role of Computers in Clinical Data Collection and Management

244

8.2.9.

Difficulties in Clinical Data Collection and Management and Use of Computers to Overcome them

245

8.3.

Regulation of Computer Systems

247

8.3.1.

Introduction

247

8.3.2.

CFR Part 11

247

8.4.

Exercise

248

Unit-V

Chapter 9: Artificial Intelligence, Robotics and Computational Fluid Dynamics

9.1.

Artificial Intelligence (AI)

250

9.1.1.

General Overview

250

9.1.2.

Evolution of Artificial Intelligence (AI)

250

9.1.3.

Advantages of Artificial Intelligence (AI)

250

9.1.4.

Disadvantages of Artificial Intelligence (AI)

251

9.1.5.

Significance of Artificial Intelligence (AI) in Pharmaceutical Industry

252

9.1.6.

Pharmaceutical Applications of Artificial Intelligence (AI)

252

9.1.7.

Challenges in Use of Artificial Intelligence (AI)

255

9.1.8.

Future Directions of Artificial Intelligence (AI)

257

9.2.

Robotics

258

9.2.1.

General Overview

258

9.2.2.

Types of Robots Used in Pharma Industry

259

9.2.3.

Advantages of Robotics

260

9.2.4.

Disadvantages of Robotics

262

9.2.5.

Significance of Robotics in Pharmaceutical Industry

262

9.2.6.

Pharmaceutical Applications of Robotics

263

9.2.7.

Current Challenges in Use of Robotics

266

9.2.8.

Future Directions of Robotics

266

9.3.

Pharmaceutical Automation

267

9.3.1.

General Overview

267

9.3.2.

Advantages of Pharmaceutical Automation

267

9.3.3.

Disadvantages of Pharmaceutical Automation

268

9.3.4.

Significance of Pharmaceutical Automation in Pharmaceutical Industry

269

9.3.5.

Pharmaceutical Applications of Pharmaceutical Automation

270

9.3.6.

Pharmaceutical Automation in Tablet Manufacturing

274

9.3.7.

Current Challenges in Use of Pharmaceutical Automation

275

9.3.8.

Future Directions of Pharmaceutical Automation

276

9.4.

Computational Fluid Dynamics

276

9.4.1.

General Overview

276

9.4.2.

Advantages of Computational Fluid Dynamics

277

9.4.3.

Disadvantages of Computational Fluid Dynamics

277

9.4.4.

Significance of Computational Fluid Dynamics in Pharmaceutical Industry

278

9.4.5.

Pharmaceutical Applications of Computational Fluid Dynamics

279

9.4.6.

Current Challenges in Use of Computational Fluid Dynamics

280

9.4.7.

Future Directions of Computational Fluid Dynamics

281

9.5.

Exercise

282