Drug Discovery Dynamics

Introduction:

The journey from a potential drug idea to a life-saving treatment is a complex and painstaking process, driven by science, innovation, and cutting-edge technology. Drug discovery is not just about finding substances that can treat diseases—it’s about using advanced methods to identify, test, and refine molecules that can have a real impact on human health. Techniques like pharmacophore analysis, QSAR modeling, molecular docking, and combinatorial methods are at the forefront of modern drug development. These tools allow scientists to predict how drugs interact with biological targets, optimize their effectiveness, and minimize side effects. In addition, models like lab rats help to test the safety and efficacy of new compounds before they reach human trials. In this blog, we’ll explore the exciting methods behind drug discovery, taking a closer look at the technologies and approaches that are transforming the way we develop new medicines.

The word "drug” is derived from the Dutch/German word "droog", which means "dry", since in the past, most drugs were dried plant parts.

Chemistry of a Drug:

A drug can be chemically classified into various categories, including:

1. Alkaloids: Derived from plants, containing nitrogen (e.g., morphine, quinine).

2. Glycosides: Containing a sugar moiety (e.g., digoxin, ouabain).

3. Amino acids: Building blocks of proteins (e.g., L-DOPA, melphalan).

4. Peptides: Short chains of amino acids (e.g., insulin, vasopressin).

5. Proteins: Large biomolecules (e.g., antibodies, enzymes).

6. Nucleotides: Building blocks of DNA/RNA (e.g., antiviral drugs).

7. Lipids: Fatty molecules (e.g., steroids, prostaglandins).

8. Terpenes: Derived from plants, containing isoprene units (e.g., taxol, artemisinin).

9. Phenolics: Containing a phenol ring (e.g., aspirin, paracetamol).

10. Heterocyclic compounds: Containing rings with multiple elements (e.g., antidepressants, antihistamines).

Difference between Drug and Medicine:

What distinguishes a medicine from a drug?

Although "medicine" and "drug" are frequently used synonymously, they have different meanings:

A substance that affects the body physiologically when ingested is referred to as a drug, a general term.

Drugs can be harmful or illegal; not all of them are meant for therapeutic use.

Drugs and other substances used in the diagnosis, treatment, prevention, or enhancement of health are specifically referred to as medicine.

As a result, not all drugs are considered to be medicines, even though all drugs are.

Examples of non-medicinal drugs:

Recreational Drugs: These are used primarily for pleasure or social reasons rather than medical purposes.

Cocaine: A stimulant that can produce intense euphoria but has significant health risks.

Ecstasy (MDMA): Known for its stimulant and empathogenic effects, often used in social settings.

Performance-Enhancing Drugs: Used to improve physical or cognitive abilities.

Anabolic Steroids: Used to enhance muscle growth and athletic performance, but can have serious side effects.

Hallucinogens: Substances that alter perception and consciousness.

LSD (Lysergic Acid Diethylamide): Known for its potent hallucinogenic effects.

Psilocybin (Magic Mushrooms): Produces hallucinatory experiences.

Cannabis: While it has potential medicinal uses, it's often used recreationally for its psychoactive effects.

These substances can have serious health risks and are often subject to legal regulations.

The ideal qualities of a drug include:

1. Efficacy: The drug should be effective in treating the intended disease or condition.

2. Safety: The drug should have a low risk of adverse effects and be safe for use in the target population.

3. Selectivity: The drug should target the specific biological mechanism or pathway involved in the disease, minimizing harm to healthy cells or tissues.

4. Potency: The drug should be effective at a relatively low dose, reducing the risk of side effects.

5. Stability: The drug should remain effective and unchanged over time, with a stable shelf life.

6. Solubility: The drug should be soluble in biological fluids, allowing for easy absorption and distribution.

7. Bioavailability: The drug should be absorbed and utilized by the body consistently and predictably.

8. Low toxicity: The drug should have a low risk of toxic effects, even at high doses.

9. Few side effects: The drug should have a minimal risk of adverse reactions or side effects.

10. Convenient dosing: The drug should have a simple and convenient dosing schedule.

11. Affordability: The drug should be reasonably priced and accessible to those who need it.

12. Consistency: The drug should have consistent quality and performance between batches.

Traditional Approaches in Drug Design:

1.      High Throughput Screening (HTS):

It is a test method, where a massive number of natural compounds are analyzed for their biological activities.

Figure 1: High Throughput Screening of different compounds expected to be potent drugs.  

2.      Active Analog Approach (AAA):

It is a test method, where a lead structure (A chemically active compound with sufficient potency for biological activity) is taken and its derivatives are developed to enhance the desired biological activity.

3.      de Novo design (DND):

Here, the new molecules are designed either based on similarities with known reference molecules or based on their complementarity with the active site of an enzyme.

It utilizes generative algorithms to design novel drugs from scratch.

Hurdles in traditional drug designing approaches:

1.      Time Consuming:

a.      Drug screening: The process of testing a large number of compounds to identify those with potential therapeutic effects requires about 2-10 years.

b.      Preclinical testing: Testing the drug in the lab and model animals like rats, mice, zebrafish, etc. require up to 6 years.

Figure 2: Preclinical Testing: Drug treatment to cells of MDA-MB 231 cancer cell line.

Figure 3: Preclinical Testing: Oral drug administration to lab rats (Rattus norvegicus).

Figure 4: Preclinical Testing : Intraperitoneal drug administration to lab rat (Rattus norvegicus).

c.       Phase I volunteer testing: Testing the drug in 20-30 healthy volunteers for the safety and dosage required up to 6-7 years.

d.      Phase II volunteer testing: Testing the drug in 100-300 patient volunteers to check the efficacy and the side effects requires up to 8-9 years.

e.       Phase III volunteer testing: Testing the drug in 1000-5000 patient volunteers to monitor reactions to long-term drug use requires up to 12 years.

f.       Review and Approval: It requires around 14 years.

g.      Post-marketing testing: It will be done after the approval and it requires almost a year extra.

2.      Trial and Error:

It is estimated that about 10,000 compounds are synthesized and screened before one successful drug is prescribed, out of which about 10 are tried.

3.      Expensive:

Out of the selected 10 compounds to be used as drugs, 7 are too expensive.

To overcome the above-described issues, there are other methods followed for drug discovery as follows-

1.      Combinatorial chemistry:

The term "combinatorial chemistry" refers to a branch of chemistry focused on the systematic assembly of a large number of diverse compounds to synthesize new compounds, which are expected to become a drug molecule.

The combined chemical compounds are then stored in the computer known as a chemical library database.

This approach allows for the rapid synthesis of many compounds simultaneously, which can then be screened for biological activity.

The process involves systematic and repetitive covalent linkages of these building blocks, enabling the generation of a vast array of molecular entities in a single experimental setup.

These libraries can be composed of small molecules, peptides, or other chemical entities, and can be generated through various synthetic methods, including solid-phase synthesis and "split and mix" techniques.

Solid-Phase Synthesis:

The solid-phase drug synthesis refers to the method of synthesizing drug or peptide molecules by attaching them to a solid surface support, typically a resin or bead.

Figure 5: Solid phase synthesis of a peptide drug.

Split and Mix Technique:

The split-and-mix synthesis involves the following steps:

Initial Splitting: A solid support (often resin beads) containing a starting material is divided into multiple equal portions.

Reaction with Reagents: Each portion is reacted with a different reagent. This step generates a variety of compounds based on the different reagents used.

Pooling and Mixing: After the reactions, all portions are pooled together and mixed. This homogenization allows for the next round of splitting.

Repetition: The process is repeated, with the pooled mixture being split again into new portions, each of which is reacted with different reagents. This cycle can continue, exponentially increasing the number of unique compounds generated with each iteration.

Figure 6: Split and Mix Synthesis of a drug.

The ability to create tens of thousands to millions of compounds in a single process significantly accelerates the drug discovery process compared to traditional methods, which typically involve the sequential synthesis and testing of individual compounds.

2.      Rational structure-based drug design:

Rational structure-based drug design is a systematic approach to developing new pharmaceuticals by leveraging the three-dimensional (3D) structures of biological targets, such as proteins.

This method contrasts with traditional drug discovery techniques by focusing on the molecular details of interactions between drugs and their targets.

The basic principle of Rational Drug design:

The concepts of rational drug design are simple and known for a long time (E. Fisher 1894, P. Ehrlich 1909) - Lock & Key hypothesis.

There is a specific target and we have to discover a specific lead molecule to block its activity.

Figure 7: Basic principle of Rational Drug Design.

Key Components of Rational Structure-Based Drug Design:

CADD (Computer Assisted Drug Design):

It encompasses a broad range of computational techniques used in drug discovery, including both structure-based and ligand-based approaches.

1. Target Identification: The process begins with identifying a specific biological target, often a protein associated with a disease. Understanding the role of this target in the disease mechanism is crucial.

Figure 8: The 3D Structure of an identified target protein.

2. Structural Analysis: Once the target is identified, its 3D structure is determined using techniques like X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. This structural information is vital for understanding how potential drugs will interact with the target.

 Figure 9: Determination of its active sites.

3. Ligand Design: With the target structure in hand, researchers design small molecules (ligands) that can bind effectively to the target's active site. This involves computational methods to  predict how different ligands will fit and interact with the target.

 Figure 10: Designing of a ligand molecule.

4. Molecular Docking: This computational technique simulates the binding of ligands to the target, allowing researchers to evaluate the strength and specificity of interactions. Various scoring functions are used to estimate binding affinities.

Figure 11: Prediction of preferred orientation of drug molecule inside the active site of the target protein (Molecular Docking).

Optimization: After identifying promising lead compounds through docking studies, these compounds undergo further modifications to enhance their efficacy, selectivity, and pharmacokinetic properties.

If the Structural Information of the target protein is unknown, then Molecular Docking cannot be done, instead, other aspects of CADD are taken into account such as QSAR and Pharmacophore Analysis.

5. QSAR (Quantitative Structure-Activity Relationship):

It correlates chemical structure with its biological activities by using statistical models to predict the effect of structural changes on enzymatic activities.

Figure 12: The QSAR Model of drug designing.

6. Pharmacophore analysis:

 A pharmacophore is defined as an ensemble of steric and electronic features necessary for optimal supramolecular interactions with a biological macromolecule, triggering or blocking its biological response.

It serves as an abstract representation that allows for the identification of structurally diverse compounds that can bind to a common receptor site.

Key Features:

Hydrophobic regions: Areas that promote hydrophobic interactions.

Aromatic rings: Structures that can engage in π-π stacking interactions.

Hydrogen bond donors and acceptors: Functional groups that can form hydrogen bonds with the target.

Charged groups: Cations and anions that can participate in ionic interactions.

Process:

Selection of Ligands: Choose a diverse set of known active and inactive compounds.

Conformational Analysis: Generate low-energy conformations of these compounds.

Molecular Superimposition: Fit the conformations to identify common features.

Abstraction: Create an abstract representation of the features.

Validation: Test the model's ability to predict biological activity across a range of compounds.

 Figure 13: Pharmacophore analysis.

Figure 14: The Ligand-Pharmacophore Mapping of drug designing.

Advantages of Rational Structure-Based Drug Design:

Efficiency: This approach allows for a more focused search for potential drug candidates, reducing the time and resources spent on less promising compounds.

Specificity: By understanding the detailed interactions at the molecular level, researchers can design drugs that specifically target the intended biological pathways, potentially leading to fewer side effects.

Integration with Computational Tools: Advances in computational chemistry and artificial intelligence have significantly enhanced the capabilities of rational drug design, allowing for the analysis of large datasets and the prediction of complex interactions.

Conclusion: 

Rational structure-based drug design represents a modern, efficient approach to drug discovery, utilizing detailed structural information to guide the design and optimization of new therapeutics. This method not only accelerates the development of effective drugs but also improves the likelihood of success in clinical applications by targeting specific biological mechanisms.

References:

Naik, P. K., Muthyala, M. K., & Kumar, A. (2011) Rational design, synthesis and biological evaluations of amino-noscapine: a high affinity tubulin-binding noscapine. Journal of Computer-Aided Molecular Design, 25(5), 531-542.

Sabe, V. T., Ntombela, T., Jhamba, L. A., Maguire, G. E. M., Govender, T., Naicker, T., & Kruger, H. G. (2021). Current trends in computer-aided drug design and a highlight of drugs discovered via computational techniques: A review. Current trends in computer-aided drug design and a highlight of drug discovered via computational techniques, A review Volume 244.

Golbraikh A.; et al. Predictive QSAR modeling: methods and applications in drug discovery and chemical risk assessment. Handbook of computational chemistry. Springer Netherlands. 2012: 1309-1342.

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https://www.frontiersin.org/