1. Bypass Blood Brain Barrier
2. DRUG DISTRIBUTION
2.1. Oral Administration
2.1.1. Dissolution
2.1.2. Gastrointestinal Mucosal Barrier
2.2. Parenteral Administration
2.2.1. Bypass Internal Barrier
2.2.1.1. Intravenous injection
2.2.1.1.1. Subcutaneous Injection
2.3. Protein Binding
2.3.1. Serum Proteins
2.3.1.1. Drug's Effective Solubility
2.3.1.1.1. Biodistribution
2.4. Tissue Depots
2.4.1. Lipophilicity
2.5. Drug Metabolism
2.5.1. First-Pass effect
2.5.1.1. Parent drug and its metabollite
2.6. Excretion
2.7. Receptor
2.7.1. Endogenous Ligands
3. SAR PRINCIPLES
3.1. Orphan Receptors
3.2. Affinity
3.3. Intrinsic Activity
4. QSAR
4.1. In silico method
4.2. Correlation
4.2.1. Chemical Structures
4.2.2. Biological Activities
4.3. Prediction of Biological Activities or Potency
4.4. Screening tool
5. COMPUTER AIDED DRUG DESIGN (NEWER METHODS)
5.1. Drug-Receptor Interactions
5.1.1. Electrostatic Interactions
5.1.2. Hydrogen Bonds
5.1.3. Van der walls forces
5.1.4. Covalent bonds
5.2. Calculated Conformations
5.2.1. Computational Methods
5.2.2. Predict the most stable conformation of molecule
5.3. Conformational Flexibility and multiple modes of action
5.3.1. adapting to different conformations
5.3.2. interacting with targets in various ways
5.3.3. helps researches to identify new targets
5.3.4. optimizing the properties of existing drugs.
5.4. Isostemerism
5.4.1. Selection of structural components
5.4.2. Steric Electronic
5.4.3. Solubility Characteristics
5.5. Optical Isomerism and Biological Activity
5.5.1. commercial drug are synthetic
5.5.2. D- and L- Isomers have same physical properties
5.5.3. Large number of drugs are diastereomers
5.6. Database searching and mining
5.6.1. Metaboloism & Toxicity
5.6.2. SAR databases
5.6.3. Virtual screening
5.6.4. Metaboloism & Toxicity
5.7. Three- Dimensional Quantitative Structire-Activity Relationships
5.7.1. 3D Shape molecule knowledge
5.7.2. Algorithms for measuring
5.7.2.1. Molecular Shape Analysis
5.7.2.2. Distance Geometry
5.7.2.3. Molecular Similarity Matrices
5.8. Drug Developed using the Newer Computer-Aided Drug Design Method
5.8.1. DORZOLAMINE
5.8.1.1. treats glaucoma
5.8.1.2. carbonic anhydrase inhibitor
6. PHYSICOCHEMICAL PROPERTIES
6.1. Acid-Base properties
6.1.1. Acid Strength
6.1.1.1. Drug Distribution and pKa
6.1.2. Percent Ionization
6.1.2.1. pH
6.1.3. Conjugate base pairs
6.1.3.1. Proton donor
6.1.3.2. Proton Acceptor
6.2. Water Solubility
6.2.1. Empiric Approach
6.2.1.1. Carbon-solubilizing potential
6.2.2. Analytical Approach
6.2.2.1. log of the partition coefficient of a molecule
6.3. Stereochemistry
6.3.1. Enantiomers
6.3.2. Diastereoisomers
7. DRUG DISCOVERY
7.1. molecular structure
7.1.1. physical
7.1.2. chemical
7.1.3. biological
7.2. CDD Correlations
8. EXAMPLES
8.1. Acetaminophen
8.2. Ibuprofen
9. COMPUTER AIDED DRUG DESIGN (EARLY METHODS)
9.1. Has QSAR been Successful?
9.1.1. Expectation Dependent
9.1.2. Should Explain Physical Reality
9.1.3. No commercially successful drugs
9.2. Other Physiochemical and Descriptor Parameters
9.2.1. Rules in maximizing substituent information
9.2.1.1. Combinations should not occur frequently
9.2.1.2. Appearance should be equal
9.2.1.3. No two substituents present
9.2.1.4. Should occur more than once
9.2.2. Physiochemical Parameters
9.2.2.1. Electronic
9.2.2.2. Steric
9.2.2.3. Partitioning Effects
9.3. Topological Descriptors
9.3.1. Graph theory
9.3.1.1. Arbitrary Numbering
9.3.1.2. No 3D Representation
9.3.1.3. Differentiate Bond and Atom Type
9.3.2. Molecular Connectivity
9.3.2.1. Limits Paths To Actual Bonds
9.3.2.2. Distinguish Heteroatoms
9.3.3. Topological Indices
9.3.3.1. Good Regression Equations
9.3.3.2. Can Be Correlated
9.4. Classification
9.4.1. Training Set
9.4.1.1. Predict Biological Activity
9.4.1.2. Represent Chemical Population
9.4.1.3. Most Retrospective Studies
9.4.1.4. Repetition is Important
9.4.2. Computer Programs Breaks Molecules
9.4.2.1. Carbonyls
9.4.2.2. Enones
9.4.2.3. Conjugation
9.4.2.4. N-substitution Patterns
9.4.2.5. Different Rings (Sizes and Types)
9.4.2.6. Aliphatic Substitution Patterns
9.4.3. Multivariate Statistics
9.4.3.1. Discrimant Analysis
9.4.3.1.1. Principal Component Analysis
9.5. Statistical Prediction of Pharmacological Activity
9.5.1. Logarithmic Value
9.5.1.1. Dependent Variable
9.5.1.2. Linearize Data
9.6. Partition Coefficient
9.6.1. Octanol or Water System
9.6.1.1. Partitioning Steps
9.6.1.1.1. Leaving Aqueous Fluids