CADD: AN ABSOLUTE REPLACEMENT FOR TRADITIONAL DRUG DISCOVERY

CADD: AN ABSOLUTE REPLACEMENT FOR TRADITIONAL DRUG DISCOVERY

Vaishali Pardeshi, Bhagyashree Pawar Gaikwad, Anuja Uday Vaidya, Khushal Madanlal Ghanchi *, Rohan Sunil Kengarkar and Tarun Singh Rajput

Abhinav Education Society College of Pharmacy, Narhe, Pune, Maharashtra, India.

ABSTRACT: CADD is a powerful method to discover possible therapeutic compounds in conventional drug discovery. It has now surpassed all other high-throughput screening options, commonly used in drug discovery and development. The creation and optimization of several clinically utilized medications have significantly advanced thanks to CADD, or computer-aided drug design. Computer-aided drug design can take one of two methods: (1) Ligand-based (Analogue based) or (2) Structure-based (Target Based). All of these methods rely on MM force fields to represent atomic-level interactions and define molecular shapes, energy, and motion. The two most common approaches to drug design are structure-based drug design and ligand-based drug design. We can learn about drug-receptor interactions from it. Structure-based drug design includes the discovery of binding sites as well as docking and stocking, virtual screening, compound selection, and lead optimization. Quantitative structure-activity relationships (QSAR), pharmacologic modeling and other processes are aspects of the structure-based drug design process. As can be seen, CADD assists in recognizing appropriate pharmacological properties and compatibility to get an advantage in pre-clinical trials.

Keywords: Drug design, Drug discovery, Molecules, CADD, Lead optimization, Lead discovery

INTRODUCTION: Computer-aided drug design (CADD) provides a variety of tools and methods to help with drug design at different phases, minimizing overall research expenditure and shortening the time it takes to develop a medication. The process of discovering new drugs and developing them is a lengthy, expensive, hazardous, and complex one with few counterparts in the business world. The pharmaceutical industry commonly uses computer-aided drug design (CADD) techniques to improve the process of drug discovery ^{1, 2}.

During the lead optimization phase of drug development, using computational methods offers a sizable economical advantage. Pharmacological research laboratories dedicate a great deal of time and resources to the various stages of drug discovery, beginning with the identification of therapeutic targets, candidate drug discovery, and drug optimization through pre-clinical and extensive clinical experiments to assess the efficacy and safety of newly developed drugs.

The largest pharma companies have made significant investments in the regular Ultra-High Throughput Screening (u-HTS) of several drug-like candidates ³. In contrast, drug design and optimization progressively use computer-based virtual screening. Technological breakthroughs in DNA microarray research analyze hundreds of genes linked to disease and can be used to learn more in-depth information about the disease targets, metabolic pathways, and pharmacological toxicity. Empirical quantum mechanics, statistical mechanics and molecular mechanics are some of the theoretical methods available. This most recent development has inspired to integrate explicit solvent effects. All of this work is based on high-end computer graphics, which are mostly supported by workstations.

Drug Discovery Process: Drug discovery is a set of procedures that, identify the drug molecules for the effective management or control of disease causes. To begin, many chemical compounds are screened to find the best disease targets. Understanding the structure of the drug receptor is required to modify the drug molecules to the binding site ⁴.

The drug discovery process starts with understanding the therapeutic index for which the drug to be designed. It consists of the following steps

Lead Drug Discovery:

• Target Drug Discovery
• Lead Identification
• Lead Optimization ⁵

Pre-clinical and Clinical trials to confirm the drug's safety, efficacy and side effects ⁶.

• Animal trials
• Clinical trials

The steps involved in getting FDA clearance for a newly discovered drug and releasing it onto the market so that the general public may utilize it ⁷.

• Additional research after commercialization
• Development of the medication

Pre-clinical development and the discovery of novel drugs often take 3-6 years. Before a product is sold on the market, it may take up to 10 years for the clinical trials to be completed. A successful drug's market entry typically takes 10–15 years and more than $1.2 billion. About 250 of the 5000–10000 evaluated compounds on average are chosen for preclinical studies. Only one of the five is authorized by the FDA after a thorough analysis of the newly found medicine. Only five of them enter clinical trials.

Major Types of Approaches in CADD:

• Direct approach / Structure-based Drug Design
• Indirect approach / Ligand-based Drug Design

Direct Approach / Structure-Based Drug Design: Structure-based drug design, often referred to as direct drug design, is based on the three-dimensional structure of the biological target, which may be established using methods like X-ray crystallography or NMR spectroscopy. It could be feasible to anticipate the experimental structure of a target if the experimental structure of a protein that is similar to the target is known. Potential drugs that are predicted to connect to the biological target with high affinity and selectivity can be developed using interactive visualizations and a medicinal chemist's intuition. Structure-based design is one of the oldest techniques for producing drugs. A tool that has benefited in the quest for new medications is structure-based drug design. We investigate the structural dynamics and electrical properties of ligands using parallel simulations ⁸. This has aided in the quick advancement of structure-based medication creation. Structure-based drug design may be loosely categorized into two groups 1. Database searches or Ligand-based Drug Design 2. Receptor-Based Drug Design.

X-ray Crystallography: A method of extremely high-resolution microscopy that improves our understanding of how proteins work by allowing us to see protein structures at the atomic level. An X-ray beam is fired at a crystal, scattering the light in many different directions. This procedure, called X-ray crystallography, is used to ascertain how atoms are arranged within a crystal. The angles and intensities of these diffracted beams may be used by a crystallographer to provide a three-dimensional picture of the density of electrons within the crystal ⁹.

X-rays are useful for studying within crystals because their wavelengths are comparable to the size of atoms. X-Ray Crystallography utilizes the regularity of a crystal's light diffraction to determine the structure of a molecule or an atom. After that, they "strike" the crystallized molecule with an X-ray beam. As the X-rays strike them, the electrons around the molecule diffract ¹⁰.

NMR Spectroscopy: Since clinically used drugs are usually natural or synthetic compounds, a quantitative approach by solution-state NMR is quite useful in determining the contamination profile of the drugs, outlining the makeup of drug products, and examining drug metabolites in bodily fluids. It is also useful in studying the trends and kinetics of proteins on solid surfaces and enzyme allostery ¹¹.

FIG. 1: TPR-LIKE FOLDING & PROTEIN FIS1 MEDIATES MITOCHONDRIAL FISSION

Homology Modelling: Any SBDD approach must have the target's 3D structure as a prerequisite. To identify the three-dimensional (3D) structure of molecules, a variety of integrated structural biology methods are available, such as single-particle cryo-electron microscopy, NMR spectroscopy, and X-ray crystallography. However, because it is technically challenging to generate, purify, or characterize proteins, it is challenging to determine the structure of a therapeutic protein. In-silico methods like homology modeling, threading, or ab initio modeling are used to approximate the three-dimensional structure of a target in the absence of any experimental structures ¹². The most reliable of the three computer methods for predicting the 3D structure of a target is homology modeling, which uses the structural features of a protein that is identical to the target in question by more than 40%. (Known as template structure). Homology modeling is based on the idea that two extremely similar sequences have equivalent structural traits. When there isn't a template structure (>40% sequence similarity) available in the protein structure database but the target sequences have the same protein fold as those of known structures, threading or fold recognition is the method of choice ¹³.

Structure-Based Drug Design:

Indirect Approach / Ligand Based Drug Design: Drug Design Based on Ligands An strategy called ligand-based drug design, which focuses on understanding of compounds that bind to the desired biological target, is employed in the lack of 3D information about the receptor.

The alternate strategy is LBDD if no structural data for the therapeutic target is available. LBDD just needs structural data and data on the bioactivity of small molecules, unlike SBDD, which also requires a previous understanding of mechanisms of action. The LBDD hypothesis states that molecules with comparable structural characteristics are likely to have related properties. A critical stage in LBDD is acquiring and preparing small molecule libraries. Chemical structures are frequently created, processed, and dealt with using molecular graphs. Atoms and bonds are represented in a molecular graph as nodes and edges, respectively, or as a combination of nodes and edges ¹⁴.

Two common methods of interacting with molecular graphs are linear notations and connection tables. A connection table has sections with details on the types of atoms, connections, and coordinates. Mol2, SDF, pdb and other file formats are examples of connection tables. Combinations of alphanumeric characters make up linear notation. The Wiswesser line notation and the SMILE (simplified molecular input line entry specification) are two examples of linear notations. When storing or sending millions of tiny molecules, linear notations are preferred to connection tables because they are more compact ¹⁵. The most popular LBDD methods are pharmacophore modeling, quantitative structure-activity relationships (QSAR) and molecular similarity-based search. Pharmacophore modeling and 3D quantitative structural activity interactions are the most significant and often utilized techniques in ligand-based drug design (3D QSAR). For lead optimization and lead discovery, they can provide suitable prediction models. Search for molecular fingerprints and structures QSAR for pharmacophore ¹⁴.

Molecular Similarity-based Search: Molecular similarity-based search is the LBDD strategy that is easiest to use to find attractive small molecules. LBDD and SBDD techniques both independently employ and include molecular similarity-based search when exploring small chemical libraries utilizing molecular descriptors. Molecular descriptors include, among many others, molecular weight, atom types, bond lengths, surface area, electro negativities, atom distributions, aromaticity indices, solvent properties, and many others. It requires experiments, the application of quantum mechanical tools, or previous knowledge to create molecular descriptors. Molecular descriptors are classified as 1D, 2D, or 3D descriptors based on their "dimensionality". One-dimensional descriptors include scalar properties of molecules such as molecular weight, logP values, and molar refractivity. 2D descriptors derived from chemical composition or configuration include topological indices and 2D fingerprints, for instance. A molecule's conformation is where 3D properties originate. Examples of 3D descriptors include electrostatic potentials, dipole moments, the highest occupied/lowest empty molecular orbital energies, and others ¹⁶.

Common Software to Predict Molecular Descriptors

• ADAPT >260 (topological, geometrical, electronic, physicochemical) ¹⁷.
• ADMET Predictor>290 (constitutional, functional group counts, topological, E-state ¹⁸.
• CODESSA >1500 (constitutional, topological, geometrical, charge related, semiempirical, thermodynamical) ¹⁹.
• DRAGON >5200 (constitutional, geometrical, topological, 2D autocorrelations, WHIM, GETAWAY, RDF, functional groups, properties, 2D binary, and 2Dfrequency fingerprints, etc.) ¹⁵.
• Pre-ADMET >955 (constitutional, geometrical, topological, physicochemical, etc.) ¹⁵.

Quantitative Structure Activity Relationship (QSAR): The link between chemical structures and biological activity may be determined using a computer technique called QSAR, as the term suggests. The quantitative structural property relationship (QSAR) technique is frequently employed in the rational drug design process, but it is also frequently used to forecast other physicochemical properties and is known as such. The underlying principle of QSAR is the possibility of linked biological activity between compounds with similar structural characteristics ²⁰.

Commonly used QSAR Methods ad descriptions are given below:

HQSAR Descriptor type: 2D

Description: Hologram by combining binary patterns with biologically relevant fingerprints, QSAR is a technique that leverages molecular substructures to produce molecular holograms ²².

CoMFA Descriptor type: 3D

Description: The steric and electrostatic properties of molecules are associated to their biological activities by comparative molecular field analysis ²³.

CoMSIA Descriptor type: 3D

Description: In addition to the parameters for steric and electrostatic contribution, comparative molecular similarity indexes also include terms for hydrophobicity, H-bond donor/acceptor, and hydrophobicity ²⁴.

Pharmacophore Modelling: A pharmacophore is a combination of "steric" and "electronic" properties that must be assembled in a three-dimensional (3D) configuration in order to interact with a certain biological target structure optimally and trigger or inhibit its biological response. The creation of ligand-based pharmacophore models is predicated on data that is currently accessible on the biological functions of drugs and ligands. Instead of representing a real molecule, ligand, or link between functional groups, a pharmacophore offers an abstract description of the properties of molecules that are important for molecular interactions with macromolecular ligands ²¹.

FIG. 2: PHARMACOPHORE

Virtual Screening: A computational approach used in drug design is virtual screening. This technique includes associating extensive chemical libraries to specific locations on target molecules, such as proteins and the substances being tested. Similar to physical screening, virtual screening aids in finding or identifying the structures most likely to bind to a pharmaceutical target (protein receptors or enzymes). The process of finding new drugs and therapeutic targets, which are generally protein receptors or enzymes, now includes virtual screening. Virtual screening scenarios prioritize improving targeted combinational libraries of accessible compounds from internal compound repositories, even though filtering the entire universe may be more feasible. Virtual screening is more efficient than traditional screening in terms of cost, time, quantity of possible substances scanned, and speed ²⁵.

Application of Computer-Aided Drug Discovery: Moving the drugs from concept to clinic: Improvements in computational technique and parallel hardware support have made it possible for in-silico methods, particularly the structure-based drug design method, to speed up the selection of new targets in the drug discovery process from the identification of hits to the optimization of the lead compound ²⁶. Through the application of this technique, certain small compounds were developed that are now in clinical trials or have received approval for medicinal use ²⁷.

CADD for Development of Potential Drugs for Neurodegenerative Disorders (NDs): The development of possible treatments for NDs provides a significant challenge because current therapies are ineffective and frequently unable to prevent neuro-degeneration. CADD has shown to be a successful method for creating medication candidates to treat neurodegenerative disorders ²⁸. It produces the most promising therapeutic candidate by removing molecules with undesirable characteristics. Drug development for Alzheimer's based on computer-aided drug design ²⁹.

New Advancement in CADD: The availability of data affects a drug discovery and development effort's efficiency. Scientific literature and case reports include a significant quantity of knowledge about chemical compounds, biological sequences, and relevant fields. These data are collected and grouped in the databases that are shown below. Numerous biological databases are described each year. In order to assist in the development of combinatorial algorithms, computational libraries are actively being created at the same time. These are the main areas where computer-aided drug design is focused ^{31, 32}.

Some small molecule databases:

1. http://pubchem. ncbi. nlm. nih. gov/ http://pubchem. ncbi. nlm. nih. gov ³³
2. ACD http://www.mdli.com ³⁴
3. https://pubs.acs.org/doi/abs/10.1021/ci049714 ³⁵
4. Ligand: https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.21467#pane-pcw-references ³⁶
5. Drug Bank Database: https://link.springer.com/article/10.1186/s13321-016-0138-2 ³⁷
6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3918487 ³⁸

CONCLUSION: The field of drug discovery and development benefits greatly from the use of computer-based drug design since it allows for the rapid and cost-effective identification of the most promising therapeutic candidates. Always encouraging for the future of drug discovery. Computer-aided medication design has helped scientists conduct a number of spectacular experiments in recent years, and it will be crucial in the coming years. Given the progress made to date, computer-aided drug design holds great promise for assisting in the search for further therapies in the future. The recent history of CADD's effectiveness in drug discovery has shown how valuable it is to the process of developing new drugs. Target molecules, lead compounds, screening, and optimization are all topics that CADD offers useful information on. QSAR and combinatorial chemistry are recent innovations. Various databases and new software tools available provide the basis for the design of ligands and inhibitors that require specificity. The CADD method is based on several techniques, design phases, docking, pharmacophore modeling, and homology modeling. Understanding the three-dimensional features of drug-molecule-based receptor interaction is another benefit of computational chemistry.

ACKNOWLEDGMENT: The authors would like to formally thank everyone who contributed to rendering this article possible. The HOD and faculty of the Abhinav Education Society's College of Pharmacy in Narhe, Pune, are also acknowledged by the authors for their valuable inputs and discussion that significantly improved the quality of this review article.

CONFLICTS OF INTEREST: The Authors declare no conflict of interest.

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    How to cite this article:

    Pardeshi V, Gaikwad BP, Vaidya AU, Ghanchi KM, Kengarkar RS and Rajput TS: CADD: an absolute replacement for traditional drug discovery. Int J Pharm Sci & Res 2024; 15(1): 12-19. doi: 10.13040/IJPSR.0975-8232.15(1).12-19.

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