Summary

Medicinal chemistry and natural products focus on the discovery, structural characterization, synthesis, modification, and biological evaluation of bioactive molecules. This collection covers synthetic drugs, natural products, pharmacophores, structure–activity relationships, molecular interactions, drug-like properties, and the chemical and biological basis of therapeutic compound development.

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Dynamic Covalent Chemistry
Dynamic covalent chemistry (DCC) designates a chemical paradigm in which molecular components are linked through covalent bonds that undergo reversible cleavage and reformation under conditions permitting thermodynamic equilibration [1]. The field was formally named by Rowan and co-workers in 2002 to describe systems that combine the robustness of covalent linkages with the error-correcting and adaptive capacity characteristic of reversible noncovalent interactions [2][3]. Within supramolecular chemistry and organic chemistry, the defining operational feature of DCC is that the bond-forming and bond-breaking processes are continuously active and responsive to external parameters such as temperature, solvent, pH, and concentration, so that the distribution of products reflects the thermodynamic minimum accessible under the chosen conditions [1]. This equilibration enables self-correction, self-sorting, and template-directed assembly, allowing simple building blocks to be converted into structurally complex architectures—including macrocycles, cages, and interlocked molecules—that would be difficult to obtain through kinetically controlled stepwise synthesis [1][3]. The dynamic covalent bonds most commonly employed include imines, disulfides, boronic esters, and acylhydrazones, each characterized by a distinct exchange mechanism and kinetic profile [1]. The term thus denotes the design logic—spanning bond selection, exchange kinetics, and equilibrium control—through which reversible covalent linkages are exploited to generate adaptive molecular systems under thermodynamic control [4].
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Topic Review
Drug-Receptor Dynamics
Drug-receptor dynamics designates the time-dependent behaviour of drug–receptor interactions, encompassing the kinetics of ligand association and dissociation and the conformational transitions through which receptors and ligand–receptor complexes interconvert among functional states. The elementary kinetic scheme describes a reversible bimolecular reaction in which a drug combines with a free receptor at an association rate constant (kon) to form a drug–receptor complex and dissociates at a dissociation rate constant (koff), with the equilibrium dissociation constant KD defined as the ratio koff/kon and serving as the quantitative measure of binding affinity [1]. The reciprocal of koff defines the residence time of the complex, a parameter that has been recognized as a determinant of therapeutic efficacy and safety that is not captured by equilibrium affinity alone [2][3]. A defining feature of drug-receptor dynamics is the dynamic character of the receptor itself: receptors are flexible entities that sample multiple conformational states, and the binding of orthosteric or allosteric ligands modulates these conformational dynamics to elicit distinct functional responses, including full, partial, inverse, and biased agonism [4][5]. The term thus denotes the kinetic and conformational description of drug–receptor systems—spanning rate constants, residence time, and state-dependent receptor transitions—as the mechanistic basis for the intensity, duration, and selectivity of drug action [2][4][6].
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Topic Review
Drug Synergy
Drug synergy denotes the phenomenon in which two or more pharmacologically active agents, when administered in combination, produce a total therapeutic effect that exceeds the effect predicted from the individual potencies of the constituent drugs under an appropriate non-interaction reference model. The concept is defined operationally through the quantitative comparison of the observed combination response against a predicted additive response: departures in which the observed effect is greater than the predicted effect are classified as synergy, whereas effects lower than predicted are classified as antagonism, with additivity constituting the null condition of no interaction [1][2]. Two principal reference models establish the null hypothesis of non-interaction. Bliss independence assumes that the drugs act by independent mechanisms, such that the combined effect is the product of the probabilities of each drug acting alone, and synergy is declared when the observed effect exceeds this multiplicative prediction [3]. Loewe additivity assumes that the drugs share a common mechanism, such that a combination of the two is equivalent to a higher dose of a single agent, and synergy is evaluated through the combination index (CI) or the isobologram: a CI less than 1 indicates synergy, a CI equal to 1 indicates additivity, and a CI greater than 1 indicates antagonism [4][5]. The Chou–Talalay method, based on the median-effect equation, provides the most widely used formalization of the CI for quantifying synergy across dose–response data [5]. A defining mechanistic feature of drug synergy is that it arises from complementary or cooperative actions on distinct targets or pathways—for example, concurrent inhibition of parallel survival signals, enhancement of drug accumulation, or circumvention of resistance mechanisms—rather than from a simple additive increase in concentration [1][2]. The term therefore designates the quantitative excess of combination efficacy over model-predicted additivity, together with the reference models, interaction metrics, and mechanistic rationales through which synergistic drug combinations are identified and characterized [1][3][4].
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Topic Review
Drug Stability
Drug stability designates the capacity of a pharmaceutical substance or dosage form, within specified limits and throughout its period of storage and use, to retain the same properties and characteristics that it possessed at the time of manufacture. The concept is operationally partitioned into chemical stability, in which each active ingredient retains its chemical integrity and labelled potency, and physical stability, in which the original physical properties—appearance, uniformity, dissolution, and suspendability—are preserved [1]. Chemical degradation proceeds through defined pathways whose relative contributions depend on the molecular structure and the formulation environment: hydrolysis of ester, amide, and lactam bonds is accelerated by moisture and adverse pH; oxidation involves reaction with molecular oxygen and is promoted by trace metals, light, and elevated temperature; photolysis results from absorption of ultraviolet or visible radiation; and epimerization, isomerization, and polymerization constitute further routes of chemical change [2]. The environmental factors that reduce stability include temperature, light, humidity, oxygen, and carbon dioxide, while the dosage-form factors include particle size, pH, solvent composition, ionic strength, container, and excipient compatibility [1]. The term therefore denotes the property of a formulated pharmaceutical product—defined by its chemical and physical integrity under defined storage conditions—together with the degradation mechanisms and formulation variables that determine the shelf-life within which that integrity is maintained [2][3][4].
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Topic Review
Drug Repurposing
Drug repurposing designates the systematic identification of new therapeutic indications for existing, failed, or investigational drug molecules that have already undergone preclinical or clinical development [1]. Within computational drug discovery methods, the term is defined by its operational logic: rather than designing a novel chemical entity de novo, repurposing exploits the known pharmacological, structural, or transcriptional profile of a pre-existing drug to infer its potential activity against a disease target or pathway for which it was not originally developed [2]. The computational methodological space of drug repurposing is organized into several complementary families: structure-based approaches that dock known drugs against new target structures; ligand- or signature-based approaches that match drug-induced transcriptional or phenotypic profiles to disease signatures; network-based approaches that infer repurposing candidates from the topology of drug–target–disease interaction networks; and matrix-decomposition or machine-learning approaches that predict drug–disease associations from large-scale heterogeneous data [1][3]. A defining characteristic of computational drug repurposing is its reliance on in silico resources—databases, predictive platforms, and knowledge graphs—that encode prior knowledge and enable the systematic prioritization of candidates for experimental validation [1][2]. The term thus denotes the computationally driven inference of new indications for known drugs, together with the algorithmic and knowledge-representation frameworks that make this inference reproducible and scalable [3][4].
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Topic Review
Drug Distribution Dynamics
Drug distribution dynamics designates the quantitative study of the reversible transfer of a drug between the systemic circulation and the tissues and organs of the body over time, describing how the concentration of the drug in each anatomical region evolves as a function of the rates of blood perfusion, capillary permeability, and tissue binding [1]. The defining conceptual framework is the compartmental model: the one-compartment model treats the body as a single kinetically homogeneous unit, the two-compartment model resolves a central (plasma and well-perfused organs) and a peripheral (poorly perfused tissues) compartment, and physiologically based pharmacokinetic (PBPK) models partition the organism into anatomically realistic organ compartments connected by blood flow, each governed by its own partition coefficient and permeability rate limit [2]. A defining parameter of distribution dynamics is the volume of distribution, which relates the amount of drug in the body to its plasma concentration and, when measured at equilibrium, quantifies the extent of extravascular distribution [3]. Within pharmacology and nanomedicine research, the term encompasses the spatiotemporal distribution of nanocarriers and their payloads, in which particle size, surface chemistry, and the biological barriers of the target tissue determine the kinetics of accumulation and the uniformity of tissue exposure [4][5].
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Topic Review
Drug Conjugate
A drug conjugate is a construct in which a pharmacologically active component is covalently joined to another molecular entity through a defined attachment or linker. The conjugation partner can provide a targeting function, alter physicochemical behaviour, or serve as a macromolecular carrier. Antibody conjugates combine antigen recognition with an attached payload, whereas polymer conjugates incorporate the drug into a polymer-associated molecular structure [1][2]. The attachment site and local chemical environment influence linker stability and the distribution of conjugate species in a preparation [3]. A cleavable linker contains a bond or sequence that releases the payload after a specified chemical or enzymatic transformation [4]. Other constructs undergo processing of the carrier to generate an active drug-containing product [1]. Drug loading, attachment-site occupancy, and the presence of unconjugated components contribute to molecular heterogeneity [1][3]. The intact conjugate, released payload, and resulting catabolites have distinct chemical identities and can differ in distribution, clearance, and interaction with biological targets [1][2][4].
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Topic Review
Drug Biotransformation
Drug biotransformation designates the enzymatic conversion of a drug molecule into one or more metabolites within the body, a process that generally increases the polarity and water solubility of the compound and thereby facilitates its elimination in urine or bile [1]. The process is conventionally organized into two sequential but functionally coupled phases: phase I reactions introduce or unmask a reactive or polar functional group through oxidation, reduction, or hydrolysis, while phase II reactions conjugate the drug or its phase I metabolite with an endogenous moiety—glucuronic acid, sulfate, glutathione, or an acetyl group—yielding a product of markedly greater polarity and molecular weight [2][3]. The phase I oxidative reactions are catalyzed predominantly by the cytochrome P450 (CYP) superfamily, membrane-bound hemoproteins that account for the majority of oxidative drug metabolism; the principal phase II conjugating enzymes include UDP-glucuronosyltransferases (UGTs), sulfotransferases, glutathione S-transferases, and N-acetyltransferases [1][3]. A defining feature of drug biotransformation is that the metabolic outcome is not uniformly detoxifying: the same enzymatic transformation can convert a pharmacologically inert prodrug into its active form or generate a reactive, toxic metabolite from a stable parent compound, a phenomenon termed bioactivation [4]. Within pharmacogenetics and drug metabolism, the term encompasses the enzyme systems, reaction types, and metabolic pathways that collectively determine a drug’s pharmacokinetic profile, its duration of action, and its potential for toxicity, together with the genetic and environmental factors that produce interindividual variability in these pathways [1][5].
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Topic Review
Drug Adsorption
Drug adsorption designates the accumulation of drug molecules at the interface between a fluid phase and a solid surface, driven by energetically favourable interactions that cause the solute to partition preferentially to the interface rather than remain in the bulk medium. The process is governed by the balance of forces between the drug, the solvent, and the solid adsorbent, and is operationally classified by the strength and nature of the surface interaction: physisorption involves weak, reversible forces such as van der Waals interactions and hydrogen bonding and typically permits multilayer formation, whereas chemisorption proceeds through covalent bonding, is often irreversible, and is restricted to a monolayer [1]. Within drug solubility and delivery systems, drug adsorption is a decisive determinant of formulation performance, because the extent and strength of drug binding to carrier surfaces such as mesoporous silica directly control drug loading, the completeness of release, and the achievable supersaturation of poorly soluble compounds [2][3]. The interaction is sensitive to the ionization states of both the drug and the surface, so that pH-dependent electrostatic attraction or repulsion modulates adsorption strength and, consequently, release behaviour [2]. The term therefore denotes the interfacial partitioning of a drug onto a solid substrate, together with the thermodynamic and kinetic parameters—isotherm models, binding affinity, and surface coverage—that quantify and predict this behaviour in pharmaceutical systems [3][4].
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Topic Review
Drug Accumulation Dynamics
Drug accumulation dynamics designates the quantitative description of how intracellular drug concentration evolves over time as a net balance between drug entry and exit rates. Drug enters via passive diffusion or carrier-mediated uptake, while removal occurs through passive leakage and active efflux transporters such as P-glycoprotein [1][2][3]. Under given conditions, these opposing processes approach a steady-state intracellular concentration at which entry equals removal [2][3]. The kinetic description comprises uptake and efflux rate constants, the transmembrane concentration gradient, intracellular binding capacity, and efflux pump saturation kinetics [4]. Within drug transport and resistance mechanisms, the concept is defined by its explanatory role: acquired resistance in cancer cells is frequently characterised by reduced steady-state intracellular drug accumulation, arising from diminished uptake or enhanced efflux capacity [2]. The term thus designates the formal, rate-based account of intracellular drug exposure—spanning influx, efflux, binding, and approach to steady state—as the kinetic determinant of cytotoxic response and resistance [1][2].
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