Drug Informatics

1 Introduction

• Drug informatics is a rapidly growing field that combines information science, data, and technology to manage drug information in clinical and research settings.
• It involves the collection, storage, evaluation, and distribution of pharmaceutical data from various sources, including primary (pharmaceutical companies, research labs, clinical observations), secondary (reimbursement and pharmacy benefit management), and tertiary (databases, reference books, journal articles).
• Drug discovery is a complex and expensive process involving the identification of novel therapeutic entities using clinical, experimental, computational, and translational approaches.
• Drug design often utilizes bioinformatics and computer modeling techniques, and it involves identifying screening hits, optimizing these hits, and applying medicinal chemistry to enhance selectivity, metabolic stability, affinity, oral availability, and effectiveness.
• Drug development begins once a molecule meets all requirements and precedes clinical trials.
• Drugs are crucial for diagnosis, prevention, and treatment of diseases, significantly impacting the quality of life and allowing individuals to manage their conditions.

2 Computational drug designing and discovery

• Drug discovery is a complex and expensive process requiring a multidisciplinary approach.
• Computational drug design (CADD) has emerged as a critical tool in drug discovery, leveraging advancements in computing power and chemoinformatics data.
• CADD streamlines drug development by using computational methodologies to accelerate the process and reduce costs.
• CADD can be categorized into two main approaches: Ligand-based drug design (LBDD) and structure-based drug design (SBDD).
• CADD is utilized for three primary reasons:
  • Filtering large compound libraries to identify smaller subsets with potential biological activity.
  • Optimizing lead compounds to achieve desirable pharmacodynamics and pharmacokinetic properties.
  • De novo drug design, where new drugs are designed from scratch.
• CADD relies on the principle that pharmacologically active compounds interact with targets like nucleic acids and proteins.
• Key factors governing these interactions include hydrophobic interactions, molecular surface, hydrogen bond formation, and electrostatic force.
• CADD helps to document and streamline the drug development process, testing substances from both natural and synthetic sources.
• CADD has contributed to the development of successful drugs like Zanamivir, Captopril, Imatinib, Dorzolamide, Nelfinavir, and Aliskiren.

3 Structure based drug designing

• Structure-based drug design (SBDD) utilizes protein 3D structure data to identify potential drug candidates.
• Key steps in SBDD include target identification, structure determination, binding affinity assessment, and drug development.
• SBDD relies on computational approaches in various fields like statistics, biophysics, biochemistry, and medicinal chemistry.
• Advances in protein structure prediction enable structure determination using techniques such as NMR, cryo-electron microscopy, and molecular dynamics simulations.
• SBDD is categorized into two main approaches: virtual screening (VS) and De novo drug development.
• Virtual screening involves searching pre-existing compound libraries for potential hits, while De novo drug development aims to design novel drug candidates based on receptor structure.
• Virtual screening uses computational methods to filter large compound datasets for potential drug candidates.
• De novo drug development focuses on discovering small fragments that fit the binding site of a target molecule.

3.1 Homology modeling ”## Homology Modeling: Extractive Summary

Here is a summary of the provided text on homology modeling:

• Definition: Homology modeling, also known as comparative modeling, predicts the structure of a protein based on its similarity to known protein structures.
• Importance: This technique is crucial for understanding protein function, as many protein structures remain undiscovered.
• Process: Homology modeling involves several steps:
  • Template selection: Identifying a known protein structure with high similarity to the target protein using tools like BLAST.
  • Sequence alignment: Aligning the target sequence with the template sequence, ensuring key residues are aligned correctly.
  • Backbone generation: Building the backbone structure of the target protein based on the template.
  • Loop modeling: Modeling the loops that connect the secondary structure elements.
  • Side-chain modeling: Placing the side chains of amino acids based on rotamer libraries.
  • Model optimization: Refining the model through energy minimization techniques.
  • Model validation: Assessing the quality of the model using stereochemical assessment and Ramachandran plot analysis.
• Limitations: Low sequence similarity between the target and template proteins can hinder the accuracy of the model. Fold recognition techniques may be used in such cases.
• Available Tools: Several servers and methods exist for homology modeling, including Modeler, I-TASSER, PRIMO, PyMod, Swiss Model, and MaxMod.
• Applications: Homology modeling is a powerful tool for various biomedical applications, offering a cost-effective and efficient way to obtain protein structures.
• Efficiency: Computational modeling typically takes less than two hours, but interpretation and visualization of the results may vary depending on user experience.

3.2 Molecular docking

• Molecular docking is a computational technique used to predict how molecules bind to target sites. It estimates the affinity of small molecules to their target based on their shapes and conformations.
• This technique is crucial in drug development and has become an essential tool for Structure-Based Drug Design (SBDD).
• Three types of docking exist:
  • Rigid docking: Both ligand and target are considered rigid.
  • Flexible docking: Both ligand and target are considered flexible.
  • Flexible ligand docking: The target is rigid, and the ligand is flexible.
• Molecular docking aims to identify the best-fitting ligands for receptor binding sites and determine their optimal binding orientations (poses).
• Two key components are needed for identifying ligand-protein interactions:
  • Search algorithm: Used to find different conformations and poses for ligands.
  • Scoring function: Evaluates the binding affinity of generated poses and ranks them.
• Tools for molecular docking like Dock, Surflex, Gold, and AutoDock have been widely used in drug discovery.
• The scoring function and sampling algorithm differentiate the various docking software available.

3.2.2 Scoring functions

• Scoring Functions are Crucial for Ligand Docking: Scoring functions play a vital role in ligand docking by evaluating and ranking predicted conformations of a ligand bound to a biomolecule.
• Two Main Scenarios for Scoring Functions:
  • Finding the Optimal Binding Pose: Scoring functions identify the docked orientation that best represents the true structure of the intermolecular complex.
  • Ranking Ligands: They not only determine the accuracy of the docking pose but also rank ligands based on their binding affinity.
• Scoring Functions Rely on Simplifications: To achieve fast computation, scoring functions make simplifications and assumptions when estimating the binding energy of a complex.
• Types of Scoring Functions:
  • Force Field Scoring Functions: Based on physical atomic interactions (e.g., electrostatic forces, van der Waals interactions) and derived from quantum mechanical calculations and experimental data. Examples include GoldScore, DockScore, and HADDOCK Score.
  • Empirical Scoring Functions: Approximate binding energies using a sum of uncorrelated terms with coefficients derived from regression analysis of experimentally obtained binding energies or structural X-ray data. Examples include LUDI, LigScore, SCORE2, and HINT.
  • Knowledge Base Scoring Functions: Utilize statistical analysis of experimentally determined atomic structures to derive frequencies of interatomic contacts between ligand and protein. Higher interaction frequency indicates stronger binding. Examples include MScore, BLEEP, and DrugScore.

3.3 Molecular simulation

• Molecular simulation (MS) is a significant scientific and engineering technique used in fields like material design and drug discovery. It allows for simulating the movement of molecules over time, providing insights into their interactions.
• MS is particularly relevant to drug development, allowing for the modeling of ligand binding, which was previously limited to a ""lock and key"" theory. This approach incorporates the flexibility of both the ligand and receptor molecules.
• The process of molecular simulation involves preparing a computer model of the molecular system and using Newton’s laws of motion to calculate the forces acting on the atoms. This involves using ""force fields,"" which are parameterized to fit experimental data and quantum-mechanical calculations.
• Commonly used software packages for molecular simulation include CHARMM, GROMACS, and AMBER. Each package offers its own strengths and weaknesses, including force field capabilities and computational speed.
• While a powerful technique, MD simulations face challenges, including the need for further refinement of force fields and the limitations of simulating long-term events (over 1 millisecond). The computational requirements for such simulations are extensive.
• Despite limitations, MS is a valuable tool for drug discovery. However, the technique needs further development to overcome challenges in predicting unbinding kinetics and sampling conformational space. Enhanced sampling approaches are being developed to address these limitations.

4 Ligand-based drug designing ”## Extractive Summary of Ligand-Based Drug Designing:

Here are some key points extracted from the provided text:

• Ligand-based drug designing (LBDD) is an indirect approach used when the 3D structure of the target protein is unavailable.
• It focuses on the relationship between the physicochemical and structural properties of ligands and their biological activities.
• LBDD utilizes data from known ligands of the target protein to develop new drug candidates.
• It relies on the assumption that molecules with similar structural characteristics have similar biological activities.
• LBDD uses molecular descriptors to represent the physicochemical and structural properties of molecules numerically.
• Pharmacophore modeling and Quantitative Structure-Activity Relationship (QSAR) are the two main approaches used in LBDD.
• QSAR aims to establish a mathematical or computational model that links structural features to biological activities.
• The basic hypothesis of QSAR: ""Similar activities are shown by compounds having similar physio-chemical and structural properties.""
• LBDD involves creating a library of lead compounds and then developing a model to predict the relationship between their structure and biological activity.
• The goal is to optimize the biological properties of compounds by modifying their structures.

4.1 Pharmacophore modeling

• Pharmacophore modeling is a concept that describes the key features of a molecule responsible for its biological activity.
• This concept dates back to Ehrlich’s work around 1800 and has been refined over time.
• Pharmacophore features are molecular patterns like anionic, cationic, hydrophobic, aromatic, hydrogen bond acceptors, and donors.
• These features are used to compare different compounds, a process called pharmacophore fingerprinting.
• Pharmacophore modeling is used in computer-aided drug design (CADD) to screen large libraries of compounds for potential drug candidates.
• Automated algorithms are used to generate pharmacophore models by aligning ligands and identifying common chemical features.
• Challenges in pharmacophore modeling include ligand flexibility and molecular alignment.
• Ligand flexibility can be addressed by pre-enumerating conformations or using on-the-fly methods.
• Molecular alignment methods can be property-based or point-based.
• Choosing the right training set is crucial for generating a successful pharmacophore model.
• Pharmacophore modeling continues to be a valuable tool in CADD due to its adaptability and potential for drug discovery.

5 ADMET

• ADMET is crucial for drug development: The goal of drug development is to create a medicine that effectively treats the targeted condition, reaches the site of action, delivers its pharmacological effects, and is cleared from the body efficiently. ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) plays a critical role in understanding how a drug interacts with the body.
• ADMET data is gathered throughout the drug development process: ADMET data is obtained from various stages, starting with early discovery and optimization, through preclinical and clinical trials. It informs decisions on chemical modifications to improve drug properties.
• Computer-based modeling is essential for ADMET prediction: In silico modeling techniques help predict ADMET properties and guide drug development, particularly in the early stages. However, these models are not always 100% accurate, but they provide valuable insights.
• QSAR and molecular modeling are key tools for ADMET analysis: Quantitative Structure-Activity Relationships (QSAR) and molecular modeling techniques are employed to analyze the relationships between a molecule’s structure and its ADMET properties.
• QSAR utilizes statistical methods to identify correlations: QSAR involves statistical analysis of physiochemical and biological data, relating molecular descriptors to ADMET parameters. This can help predict various properties, like lipophilicity, distribution volume, and clearance.
• Molecular modeling uses techniques like protein modeling and pharmacophore analysis: Molecular modeling utilizes approaches such as protein modeling, quantum mechanics, and pharmacophore modeling to understand interactions between drugs and proteins involved in ADMET processes.
• Challenges in ADMET prediction: While significant progress has been made, predicting specific pharmacokinetic parameters like distribution volume and clearance directly from molecular structure remains challenging due to limited available data.
• The importance of training set size in model development: The choice of model and its predictive accuracy are influenced by the size of the training set used for model development.

5.1 Adsorption ”## Extractive Summary:

Here is an extractive summary of the provided text:

• Drug Absorption and Administration: Drugs typically need to enter the bloodstream to reach their target cells. Common administration routes are oral and intravenous, with intravenous injection bypassing the absorption phase.
• Drug Transport Mechanisms: Drugs pass through membranes via various mechanisms: active diffusion (requires energy), passive diffusion (high to low concentration), endocytosis (transporting large molecules), and facilitated diffusion (carrier proteins).
• Bioavailability: The percentage of a drug that reaches the bloodstream in its active form. Intravenous injection achieves 100% bioavailability, while other routes result in lower bioavailability due to factors like metabolism and excretion.
• Factors Affecting Absorption: Solubility, molecular weight, ionization, and other physicochemical properties influence drug absorption.
• First-Pass Effect: After oral absorption, the first-pass effect (metabolism in the liver) can significantly reduce the bioavailability of a drug.

5.2 Distribution

• Distribution of Drugs: After absorption, drugs travel from the initial site to various body tissues (organs, muscles, etc.).
• Distribution Mechanisms: Primarily through circulation, but also possible via cell-to-cell transfer.
• Factors Affecting Distribution:
  • Polarity: Affects how easily a drug crosses membranes.
  • Molecular Size: Larger molecules distribute less readily.
  • Blood Flow: Areas with higher blood flow receive more drug.
  • Serum Protein Binding: Binding to proteins can limit distribution.
• Barriers to Distribution: The blood-brain barrier can restrict drug entry to the brain.
• Research Methods:
  • Drug Transporter Studies: Identify proteins involved in drug movement.
  • Permeability Testing: Assess how easily a drug enters cells.

5.3 Metabolism

• Drug metabolism is the process of transforming drugs into water-soluble metabolites, primarily in the liver, for excretion.
• The main enzyme involved in drug metabolism is cytochrome p450.
• Metabolites are the new substances formed during drug metabolism.
• Metabolites are often pharmacologically inactive, reducing the drug’s effect.
• Drug metabolism involves enzymes and requires research to identify key metabolites and pathways.
• Metabolic pathways can lead to toxicity by creating harmful byproducts.
• Adverse Outcome Pathway (AOP) analysis helps determine potential toxicity and safety of a drug candidate.
• Specific drug metabolism research focuses on:
  • Characterizing metabolites.
  • Assessing metabolic stability.
  • Identifying metabolites across species, especially in humans.

5.4 Excretion

• Excretion: The process of eliminating metabolized drug compounds from the body, primarily through the kidneys.
• Excretion Routes: While primarily through the liver and kidneys, excretion can also occur through tears, sweat, and breath.
• Importance of Excretion: Efficient excretion is crucial to prevent the accumulation of foreign substances, which can negatively impact normal metabolism.
• Research Focus: Scientists are studying drug excretion pathways to understand how drugs leave the body and how quickly they are excreted. Factors like molecular charge and size influence these pathways.
• Incomplete Excretion: Not all drugs are completely excreted, and the accumulation of metabolites or by-products can lead to adverse effects.
• ""In Vivo Excretion"" Research: This research aims to identify drug excretion routes, characterize drug clearance, and monitor drug and metabolite levels in different body compartments.
• Radiolabeled Molecules: These are used in animal mass balance research to quantify the excretion rate and pathways of drugs, specifically examining feces and urine.
• Further Research: Additional studies are being conducted to examine lymphatic partitioning rates, excretion via milk, and biliary excretion.

5.5 Toxicity

• Toxicity is a major factor in drug development failures: Approximately 20-40% of research drug development failures are estimated to be due to toxicity concerns.
• In silico technologies aim to predict toxicity: These technologies can be categorized into two approaches:
  • Expert Systems: These systems use knowledge from scientific literature and experts to create models.
  • Structure Descriptors and Statistical Analysis: This approach uses chemical structure descriptors and statistical correlations to predict toxicity.
• Data quality is crucial for in silico techniques: The limited accessibility of toxicity data has restricted the quantity of toxicological endpoints predicted by commercially available systems.
• Current focus on mutagenicity and carcinogenicity: Modern software packages primarily focus on predicting mutagenicity and carcinogenicity.
• Expansion to other endpoints: While some programs incorporate knowledge bases and models for additional endpoints like sensitization, irritation, neurotoxicity, immunotoxicology, and teratogenicity, these are less common.

6 Drug repurposing

• Drug Repurposing: A strategic approach to find new uses for existing drugs, potentially reducing development time and costs.
• Benefits of Repurposing:
  • Reduced risk of failure due to prior safety data.
  • Shorter development timelines.
  • Lower investment costs.
• Repurposing Strategies:
  • On-target: Utilizing known drug molecules on a new therapeutic indication, targeting the same biological target but for a different disease. (Example: Minoxidil for both hypertension and hair loss).
  • Off-target: Utilizing drugs that work on novel targets for new therapeutic indications. (Example: Aspirin for pain relief and blood coagulation suppression).
• Repurposing Approaches:
  • Experimental-based (Activity-based): Screening drugs for new uses based on experimental studies.
  • In silico: Virtually screening large drug libraries using computational methods to identify potential bioactive molecules.
• Repurposing Methodologies:
  • Disease-oriented: Utilizing disease model data, including genomics, metabolomics, and proteomics, to identify potential repurposed drugs.
  • Target-oriented: High-throughput screening of drugs based on their interactions with specific protein biomarkers or molecules of interest.
  • Drug-oriented: Based on understanding the biological effects, toxicity, and structure of drug molecules, particularly those with unknown biological targets.
• Commercial Advantages:
  • Lower investment costs for pharmaceutical companies.
  • Faster drug development and approval.
  • Potential for treating neglected diseases with limited market options.