Lecture 4: Drug Discovery Process: From Target to Therapy
Lecture Introduction
The lecture will cover the drug discovery process, focusing on a specific drug's discovery story as an example, leading into diverse methods to be discussed later in the course. The aim is to provide a comprehensive overview of how new therapeutic agents are identified, developed, and optimized.
Exam-Related Questions
Exam questions regarding old presentations will focus on general concepts and their application to the course material, rather than demanding recall of specific, minute details from research papers (e.g., precise GPCRs or protein names). The emphasis will be on understanding underlying principles.
Fundamental Principles of Drug Action
3.1. Enzyme Competition and Synergy Recap
3.1.1. Potency Comparison
Non-competitive and uncompetitive mechanisms of enzyme inhibition generally exhibit higher potency compared to competitive mechanisms. This distinction arises because non-competitive and uncompetitive inhibitors do not directly compete with higher concentrations of the natural substrate for binding to the enzyme's active site. Instead, they bind to different sites (allosteric or enzyme-substrate complex), allowing them to exert their inhibitory effect more effectively, often irrespective of the substrate concentration, or by stabilizing less active conformational states of the enzyme.
3.1.2. HIV Reverse Transcriptase Example
3.1.2.1. Enzyme Structure
HIV reverse transcriptase (RT) is a crucial viral enzyme responsible for reverse transcribing the single-stranded RNA genome of HIV into double-stranded DNA. This DNA is then integrated into the host cell's genome, a critical step for viral replication. The enzyme's structure is often described as resembling a right hand, comprising distinct domains referred to as the thumb, palm, and fingers. This intricate structure allows for a high degree of flexibility and undergoes significant allosteric movement between these domains during its catalytic cycle, essential for its polymerase and RNase H activities.
3.1.2.2. Competitive Inhibitor (AZT)
AZT (azidothymidine), also known as Zidovudine, was one of the first antiretroviral drugs approved for the treatment of HIV infection. It functions as a nucleoside analogue. AZT, once phosphorylated intracellularly to AZT-triphosphate, mimics a natural deoxyribonucleotide triphosphate, which is a substrate for reverse transcriptase. It binds to the enzyme's active site and competes with endogenous nucleotides for incorporation into the nascent viral DNA chain. Upon incorporation, the azido group at the position prevents the addition of further nucleotides, effectively terminating the growing DNA chain by incorporation, thus inhibiting viral DNA synthesis.
3.1.2.3. Non-Competitive/Allosteric Inhibitor (Nevirapine)
Nevirapine represents a class of non-nucleoside reverse transcriptase inhibitors (NNRTIs). Unlike AZT, Nevirapine does not bind to the catalytic active site of HIV reverse transcriptase. Instead, it binds to a distinct, allosteric 'pocket' located remotely from the active site, specifically within the 'palm' domain of the enzyme. This binding induces a conformational change that prevents the necessary allosteric movement and flexibility of the reverse transcriptase domains, which are crucial for its catalytic function. By altering the enzyme's shape and dynamics, Nevirapine inhibits enzyme activity without competing for the natural substrate's binding site.
3.1.2.4. Synergy
A significant therapeutic advantage often observed in HIV treatment is the synergistic effect achieved by combining drugs like AZT and Nevirapine. Because these two drugs act via fundamentally different mechanisms on the same enzyme (one competitive at the active site, the other non-competitive/allosteric), their combined effect is greater than the simple sum of their individual effects. This synergy allows for lower doses of each drug to be administered, potentially reducing side effects while maintaining or enhancing therapeutic efficacy and creating a higher genetic barrier to resistance by requiring multiple mutations for the virus to escape both drugs.
3.2. Receptor Occupancy vs. Biological Effect (Spare Receptors)
3.2.1. Non-Linear Relationship
The relationship between the percent receptor occupancy (the fraction of total receptors bound by a ligand) and the resulting biological effect or physiological response is often highly non-linear. This means that a small change in receptor occupancy can lead to a disproportionately large change in effect, or vice versa.
3.2.2. Spare Receptor Phenomenon
This non-linearity often manifests as the 'spare receptor phenomenon'. This occurs when the maximum biological effect () of a ligand can be achieved by occupying only a fraction of the total available receptors in a system. The remaining unbound receptors are termed 'spare receptors'. These spare receptors are not functionally redundant but contribute to ensuring that low concentrations of ligand can elicit a maximal response, thereby increasing the system's sensitivity to the ligand.
3.2.3. Calcitonin Receptor Example
In studies using HEK (Human Embryonic Kidney) cells expressing the calcitonin receptor, increasing concentrations of calcitonin (the natural ligand) lead to a dose-dependent proton release (a measurable biological effect). When comparing the dose-response curve for proton release (effect) to the curve for receptor occupancy, it is typically observed that the effect curve is shifted significantly to the left compared to the receptor occupancy curve. This leftward shift clearly indicates that a maximal biological effect (e.g., maximum proton release) is achieved at calcitonin concentrations where only a sub-maximal fraction (e.g., 10-20 ext{%}) of the total calcitonin receptors are occupied. This is a classic demonstration of spare receptors, showing the system's efficiency and signal amplification.
3.2.4. Gv-1 21 Inhibitor Example
An irreversible inhibitor, such as Gv-121 (hypothetical or specific compound in a given context), can be used to progressively inactivate or 'knock out' receptors one by one. As the concentration of such an inhibitor increases, it binds irreversibly to spare receptors. Initially, this leads to a rightward shift of the agonist dose-response curve (meaning a higher concentration of the agonist is needed to achieve the same effect), but the maximal response () remains unchanged. This occurs because there are still enough functional 'spare' receptors to elicit a full response. However, once a critical number of spare receptors have been inactivated by the irreversible inhibitor, the system transitions from 'spare receptor' conditions to a state where receptor availability genuinely limits the maximal response. At this point, further increases in inhibitor concentration will not only shift the curve to the right but also reduce the maximal response ( ), providing quantitative evidence of the presence and number of spare receptors.
Quantifying Drug Interactions: Isobolgrams
4.1. Definition
An isobologram is a powerful graphical tool used to visualize and quantify drug synergy or antagonism. It is a correlation plot that shows the specific doses of two different drugs (Drug A and Drug B) required, either individually or in combination, to achieve a predetermined, fixed biological effect (e.g., 50 ext{%} of the maximal response or 50 ext{%} inhibition, commonly denoted as ).
4.2. Construction
4.2.1. Determine Individual Effective Doses
First, the effective dose (e.g., or ) for each drug (Drug A and Drug B) is determined when administered as a single agent. These values represent the concentration of each drug required to elicit the chosen effect alone.
4.2.2. Plot Additive Line (Isoline of Additivity)
These individual effective doses are then plotted on the respective axes of a graph (e.g., Drug A on the x-axis, Drug B on the y-axis). A straight line is then drawn connecting these two points. This line is known as the 'isoline of additivity' or 'additive line'. Any point on this line represents a purely additive effect, meaning the combined effect of the two drugs is exactly what would be expected from the sum of their individual effects, with no interaction (neither positive nor negative).
4.2.3. Mix Drugs and Measure Combined Effect
The two drugs are then mixed at various fixed ratios of concentrations, and the combined dose (total concentration of the mixture) required to achieve the predetermined effect (e.g., or ) is measured. Importantly, the contribution of each drug in the mixture to the overall dose is plotted.
4.2.4. Plot Combined Doses and Interpret
The combined doses are then plotted on the same graph as the isoline of additivity. The position of these data points relative to the additive line determines the nature of the interaction:
If the data points fall below the additive line, the drugs are synergistic (supra-additive). This means a lower-than-expected total dose of the combination is required to achieve the fixed effect, implying that the drugs enhance each other's activity.
If the data points fall above the additive line, the drugs are antagonistic (sub-additive). This indicates that a higher-than-expected total dose of the combination is required, suggesting that the drugs reduce each other's efficacy.
If the data points fall on or very close to the additive line, the drugs exhibit a simple additive effect, meaning their combined action is merely the sum of their individual effects without any notable interaction.
4.3. Clinical Relevance
Synergy is a highly promising area for drug combinations, particularly in therapeutic fields such as oncology (cancer treatment) and infectious diseases (e.g., HIV, tuberculosis, bacterial infections). The ability to achieve a desired therapeutic effect with lower individual drug doses in a synergistic combination can lead to several benefits: potentially reduced toxicity and side effects for patients, overcoming drug resistance, and achieving more potent or specific effects that might not be possible with single agents. This strategy helps to lower the overall drug burden on the patient while maintaining or improving efficacy by attacking different pathways or mechanisms simultaneously.
Drug Discovery and Development Strategies
5.1. Overall Process
Drug discovery and development is an exceptionally complex, multidisciplinary, and lengthy endeavor, typically spanning years and costing billions of dollars. It generally involves several sequential phases:
5.1.1. Target Identification and Validation
Identifying a biomolecule (e.g., a protein, enzyme, receptor, gene) that plays a critical role in a disease pathway and confirming that modulating its activity can lead to a therapeutic effect.
5.1.2. Hit Identification
Finding initial compounds (hits) that show activity against the validated target. This often involves high-throughput screening (HTS) of large compound libraries.
5.1.3. Lead Identification and Optimization
Hits are refined into 'lead' compounds with improved potency, selectivity, and drug-like properties. Lead optimization involves medicinal chemistry efforts to enhance pharmacokinetic (ADME: Absorption, Distribution, Metabolism, Excretion) and pharmacodynamic properties while minimizing toxicity.
5.1.4. Preclinical Studies
Extensive in vitro (cell-based or biochemical) and in vivo (animal models) studies to assess the drug's efficacy, toxicity, pharmacokinetics, and mechanism of action before human testing.
5.1.5. Clinical Trials
Human testing in three phases:
Phase I: Focuses on safety, tolerability, and pharmacokinetics in a small group of healthy volunteers or patients.
Phase II: Evaluates efficacy, optimal dosage, and further assesses safety in a larger group of patients with the target disease.
Phase III: Confirms efficacy and monitors for adverse reactions in large, diverse patient populations, often comparing the new drug to existing treatments.
5.1.6. Regulatory Approval and Post-Market Surveillance
Approval by regulatory bodies (e.g., FDA, EMA) and continued monitoring of the drug's safety in the general population.
5.2. Primary Focus (within the process)
The emphasis in the initial stages of drug discovery is often on how to identify an initial active compound (a 'hit') that interacts with the target and how to transform that hit into a viable lead compound. This involves a blend of biology, chemistry, and computational science to navigate the immense chemical space and find molecules with therapeutic potential.
5.3. Structure-Based Lead Discovery & Optimization
Structure-based drug design (SBDD) is a rational approach that leverages detailed three-dimensional structural information of a biological target (typically a protein, such as an enzyme or receptor) to design and optimize high-affinity, selective ligands. This method significantly improves efficiency compared to traditional random screening.
5.3.1. High-Resolution Structural Elucidation
The first step involves obtaining a high-resolution structure of the target, usually determined by X-ray crystallography, cryo-electron microscopy (cryo-EM), or nuclear magnetic resonance (NMR) spectroscopy. This structure reveals the architecture of the binding site, including the nature and spatial arrangement of key residues involved in ligand interaction.
5.3.2. Ligand Design/Identification
Based on the binding site geometry and physiochemical properties:
De Novo Design: Computational algorithms can 'grow' or 'build' potential ligands atom-by-atom within the binding pocket, designing entirely new chemical entities.
Fragment-Based Drug Discovery (FBDD): Small molecular fragments (low molecular weight compounds) that bind weakly but specifically to different subsites within the pocket are identified (e.g., by X-ray screening or NMR) and then linked or grown to create potent ligands.
Virtual Screening/Docking: Large databases of known compounds are computationally 'docked' into the binding site to predict binding affinity and pose, identifying promising hits for experimental testing.
5.3.3. Lead Optimization
Once initial hits or leads are identified, SBDD guides the optimization process. By repeatedly determining the crystal structures of lead compounds bound to the target, medicinal chemists can visualize the precise interactions, identify areas for improvement (e.g., stronger hydrogen bonds, better hydrophobic contacts, improved shape complementarity), and rationally modify the lead molecule to enhance:
Potency and Affinity: Improving the strength and specificity of binding to the target.
Selectivity: Minimizing off-target binding to reduce unwanted side effects.
Metabolic Stability: Ensuring the drug is not too rapidly broken down in the body.
Pharmacokinetics: Optimizing absorption, distribution, metabolism, and excretion for favorable drug exposure.
Physicochemical Properties: Adjusting solubility, permeability, and stability.
This iterative cycle of design, synthesis, structural determination, and testing is central to SBDD.
5.4. Example of Drug Discovery: Target-Based HIV Protease Inhibitors
The development of HIV protease inhibitors is a landmark success story in SBDD, profoundly transforming HIV/AIDS treatment from a fatal disease into a manageable chronic condition. It stands as a testament to the power of rational drug design.
5.4.1. Target Choice and Validation
HIV protease was identified as a critical and indispensable enzyme for the viral life cycle. After HIV virions bud from an infected host cell, they are initially immature and non-infectious. HIV protease is solely responsible for cleaving a large, inactive viral polyprotein (the Gag-Pol polyprotein) into individual, functional viral proteins, including reverse transcriptase, integrase, and various structural Gag proteins, which are essential for forming mature, infectious progeny virions. Without this precise proteolytic processing, the virions remain immature andnon-infectious, effectively halting the spread of the virus. Thus, inhibiting HIV protease emerged as a highly attractive and validated therapeutic target.
5.4.2. Structural Elucidation
A pivotal breakthrough occurred in the late when the high-resolution three-dimensional () atomic structure of HIV protease was successfully determined through X-ray crystallography. This revealed that HIV protease functions as a C2-symmetric homodimer, meaning it is composed of two identical protein subunits. The active site, where catalysis occurs, features two highly conserved aspartic acid residues (one from each subunit) that are directly responsible for mediating the peptide bond cleavage. A key structural feature identified was the presence of flexible 'flaps' that cover the active site. These flaps undergo significant conformational changes, opening to allow substrate entry and then closing to optimally position the substrate for catalysis. This detailed structural information was absolutely critical; it provided an unprecedented molecular blueprint for rational drug design.
5.4.3. Rational Drug Design (Transition State Mimicry)
The central insight for designing potent HIV protease inhibitors was to leverage the concept of transition state mimicry. Enzyme inhibitors that resemble the high-energy transition state of a reaction, rather than just the substrate, typically bind with significantly higher affinity and greater stability to the enzyme. Researchers focused on designing molecules that would mimic the tetrahedral intermediate formed during the peptide bond hydrolysis catalyzed by aspartic proteases. This involved incorporating specific non-hydrolyzable functional groups, such as a hydroxylamine or hydroxyethylamine moiety, into the inhibitor scaffold. These groups structurally mimic the key features of the transition state intermediate, allowing the inhibitors to bind exceptionally tightly to the active site and effectively 'trap' the enzyme in an inactive conformation.
5.4.4. Lead Generation and Optimization
Based on these transition state mimics, early lead compounds were identified. Medicinal chemists then embarked on an intensive and iterative optimization process, heavily guided by newly obtained X-ray crystal structures of these lead compounds bound to HIV protease. This structural feedback loop allowed them to visualize the precise molecular interactions between the inhibitor and the enzyme at an atomic level. This guided systematic modifications to the inhibitor's chemical structure to achieve several crucial goals:
Enhance Affinity and Potency: Through careful chemical modifications, researchers aimed to optimize binding interactions (e.g., forming stronger hydrogen bonds, maximizing favorable hydrophobic contacts, improving shape complementarity, and fine-tuning van der Waals forces) with specific pockets and sub-sites within the active site and its surrounding regions, leading to picomolar to nanomolar potencies.
Improve Selectivity: A critical challenge was to design inhibitors that specifically targeted HIV protease while minimizing binding to homologous human aspartic proteases (e.g., Cathepsin D, Renin) to avoid off-target side effects and toxicity. Structural differences between viral and human proteases were exploited.
Optimize Pharmacokinetics: Modifications were made to improve 'drug-like' properties essential for clinical success, including enhancing oral bioavailability (how well the drug is absorbed after oral administration), reducing susceptibility to rapid metabolic breakdown (e.g., by cytochrome P450 enzymes in the liver), and ensuring an appropriate systemic exposure and plasma half-life.
Address Drug Resistance: As the virus rapidly mutates, resistance can emerge. To counteract this, inhibitors were sometimes designed with structural flexibility or, more commonly, drugs were used in combinations (e.g., co-administration of a protease inhibitor with Ritonavir, which acts as a potent cytochrome inhibitor to 'boost' the levels of other protease inhibitors by reducing their metabolism) to achieve higher drug concentrations and provide a higher genetic barrier to viral escape.
5.4.5. Clinical Success
This rational, structure-guided approach unequivocally led to the rapid development and subsequent regulatory approval of multiple highly effective HIV protease inhibitors (such as Saquinavir, Ritonavir, Indinavir, Nelfinavir, Amprenavir, and Lopinavir). When these protease inhibitors were strategically combined with reverse transcriptase inhibitors, they formed the cornerstone of what is known as highly active antiretroviral therapy (HAART). HAART dramatically reduced viral load in patients to undetectable levels, significantly improved patient survival rates, and fundamentally transformed the prognosis of HIV/AIDS, moving it from a rapidly fatal disease to a manageable chronic condition. This achievement represents one of the most significant triumphs in modern pharmaceutical science, directly attributable to the principles of SBDD.