Pancreatic Lipase Inhibitors: Mechanistic Foundations, Structural Insights, and Emerging Medicinal Chemistry Strategies

Introduction

Pancreatic lipase (PL) is the principal enzyme responsible for hydrolyzing dietary triglycerides into absorbable free fatty acids and monoglycerides.¹ Being the rate-limiting step of lipid assimilation, PL is a promising pharmacological target in reducing the caloric uptake and controlling obesity. However, the theoretical simplicity of the approach to inhibition of fat-digestion has not been easily converted into the safe and effective pharmacotherapy. Orlistat, the sole clinically approved inhibitor is constrained by potency to tolerability trade-offs, gastro intolerability and insignificant weight loss in the long term.²

Clinical evidence reveals that orlistat achieves modest weight reduction of approximately 2.9 kg beyond placebo over 12 months, with only 37-54% of patients maintaining adherence due to gastrointestinal adverse events including steatorrhea, fecal urgency, and fat-soluble vitamin malabsorption.² The disconnect between biochemical potency (IC₅₀ 0.092 μM) and clinical outcomes highlights a fundamental challenge: complete lipase inhibition triggers compensatory mechanisms including increased ghrelin secretion, altered satiety signaling, and behavioral adaptation that diminish long-term efficacy.^2,3 This potency-to-tolerability paradox necessitates a paradigm shift from maximal enzyme blockade toward controlled, partial inhibition strategies that preserve physiological lipid digestion while reducing caloric absorption.

Recent advances in structural biology, computational chemistry, natural-product exploration, and medicinal chemistry have revitalized interest in next-generation lipase modulators.³ A deeper understanding of interfacial activation, lid-domain dynamics, the colipase interaction surface, and substrate selectivity has uncovered new allosteric and partially inhibitory opportunities.^1,4 In parallel, modern high-throughput screening, scaffold hopping, structure-guided design, and AI-assisted chemistry have expanded the chemical space⁵ of inhibitors far beyond β-lactones.⁶

This review synthesizes fundamental mechanistic insight into PL activation and catalysis, summarizes key natural and synthetic inhibitor classes, and highlights emerging medicinal-chemistry strategies — including allosteric modulation, multi-target hybrid scaffolds, and non–β-lactone electrophiles — that may overcome the limitations of classical inhibition.

Enzymatic Mechanism and Structural Biology

Figure 1: Structural and mechanistic overview of human pancreatic lipase (hPL). A. Interfacial activation via lid opening and colipase binding. B. Catalytic machinery and modes of inhibitor binding.     Click here to view Figure

Interfacial Activation

A defining feature of pancreatic lipase is interfacial activation, the dramatic increase in catalytic activity observed when the enzyme encounters a lipid–water interface. In aqueous solution, the active site is shielded by a mobile lid domain, which adopts a closed conformation. Upon interaction with lipid droplets, conformational rearrangements shift the lid into an open state, exposing the catalytic machinery and forming a hydrophobic plateau optimized for substrate binding.⁷

It is activated by the action of colipase, which is a small cofactor that anchors PL to the mixed micelles in the presence of inhibitory bile salts. The interaction between the lid, colipase, and the lipid interface is critical to the effective turnover of the substrate as well as being a good target of interest when designing allosteric or partial inhibitors which seek to moderate activity rather than to block it.

Catalytic Triad and Mechanistic Chemistry

Like other serine hydrolases, PL employs a Ser–His–Asp catalytic triad (Ser152–His263–Asp176).⁸ Catalysis proceeds through:^9,10

Nucleophilic attack by Ser152 on the ester bond of the triglyceride, forming a tetrahedral intermediate stabilized by an oxyanion hole.

Acyl-enzyme formation after collapse of the intermediate and release of the first product.

Deacylation, where water (activated by His263) cleaves the acyl–enzyme intermediate to regenerate Ser152.

PL exhibits strong regiospecificity, preferentially hydrolyzing ester bonds at the sn-1 and sn-3 positions, yielding 2-monoglycerides as dominant products. This selectivity is governed by steric constraints within the active site and by subtle lid-domain positioning that guides substrate orientation.¹¹

Colipase Interaction and Structural Stabilization

In the intestinal lumen, bile salts form mixed micelles that can displace lipase from the lipid surface. Colipase overcomes this by binding tightly to PL, stabilizing the open lid conformation and anchoring the enzyme to micelles. The PL–colipase complex provides a broader hydrophobic surface for substrate entry. Structurally, co-crystalized complexes (e.g., PDB 1LPB and 1LPS) show how colipase contacts the C-terminal domain and helps maintain the catalytic configuration under physiological conditions.^12,13

Crystal Structures and Conformational States

Multiple high-resolution structures have revealed the conformational flexibility of PL:¹⁴

1LPB: PL–colipase complex, lid closed⁴

1LPS: PL–colipase with a phosphonate inhibitor, lid open¹⁵

1ETH: PL bound to a substrate analogue¹⁶

1N8S: Lipase-colipase-bile salt micelle complex¹

Together, these structures provide an invaluable foundation for rational inhibitor design, illuminating both the catalytic pocket and distal allosteric regions that may be exploited for partial inhibition

Classes of Pancreatic Lipase Inhibitors 

Pancreatic lipase inhibition has been explored across multiple chemical spaces, reflecting the enzyme’s complex interplay of hydrophobic substrate binding, interfacial activation, and unique catalytic architecture.⁸

Effective inhibitors must reconcile two competing biochemical principles:³

High affinity for a deeply hydrophobic acyl-binding tunnel, and

Selective engagement with the catalytic machinery of Ser152–His263–Asp176 without inducing systemic exposure.

These dual constraints give rise to several distinct inhibitor classes, each shaped by specific structural, mechanistic, and physicochemical requirements.

β-Lactones — Covalent, Mechanistically Precise, but Carrying a Potency–Tolerability Paradox

β-Lactones represent the clearest chemical mimicry of the transition state for ester hydrolysis, explaining their unparalleled potency.¹⁷ Their strained four-membered ring positions the carbonyl exactly where the catalytic Ser152 nucleophile attacks, allowing rapid formation of a tetrahedral intermediate mimic that collapses into a stable covalent acyl-enzyme.¹⁸

Concrete mechanistic reasons for their potency

The electrophilic β-lactone carbonyl is perfectly aligned with the oxyanion hole (Phe77, Leu153).¹⁷

The long aliphatic side chain mimics a fatty acyl tail, optimally filling the lipophilic tunnel.

Side-chain stereochemistry determines whether the carbonyl oxygen can “sit” in the oxyanion hole in a transition-state–like geometry.¹⁹

Potency range

Most β-lactones show inhibition in the low nanomolar to sub-micromolar region.¹⁹

Concrete limitation

Their irreversible covalent nature and hydrophobicity lead to:²

Local gastrointestinal (GI) accumulation

Broad suppression of fat digestion

Tolerability issues (steatorrhea, oily spotting, dose-limiting symptoms)

This is why medicinal chemistry efforts increasingly target reversible or partially reversible alternatives.

Natural Product–Derived Inhibitors — Potent, Polyfunctional Chemotypes with Drug-Likeness Challenges

Natural products offer structural diversity not easily achievable synthetically. Their molecular frameworks often provide precisely positioned aromatic rings and hydrogen-bonding groups, enabling them to compete with orlistat in binding efficiency despite being reversible.

Flavonoids & Flavan-3-ols (Concrete SAR advantages)

Flavones and flavan-3-ols demonstrate inhibition by non-covalent stabilization of the lipase active site,²⁰ mainly by

π–π stacking with Phe77 and Tyr114²¹

Hydrogen bonding to backbone NH groups forming the oxyanion hole.²⁰

Hydrophobic insertion of phenyl rings into the acyl-binding tunnel.²¹

Potency range

Flavones: 50–200 µM²²

Galloylated catechins: 0.05–2 µM (exceptionally potent for natural products)²³

Why flavonoids matter

They illustrate that reversible, non-covalent scaffolds can be potent if structural rigidity and hydrophobic patch distribution are optimized.

Aurones — Undervalued but Mechanistically Rational Scaffolds

Aurones, with their benzofuranone core, align well with the lipase catalytic groove:

Their carbonyl oxygen orients toward the oxyanion hole.²⁴

The benzofuranone ring stacks against Phe77.^4,24

Long alkoxy chains (C6–C10) mimic natural fatty acids and do not trigger covalent chemistry.^4,25

Potency

Often 1–10 µM, with synthetic variants dipping <1 µM.²⁵

Why important

Aurones bridge natural and synthetic space: they are rigid, planar, and lipophilic enough to bind well without needing an electrophilic warhead.

Chalcones — Tunable, Conjugated, and Lipase-Compatible

The α,β-unsaturated ketone moiety of chalcones provides:²⁶

Anchor points for hydrogen bonding

A conjugated π system for aromatic stacking

Highly modifiable 2′,4′-OH pattern for binding orientation.²⁶

Space for long-chain hydrophobic tail addition.²⁷

Potency

Ranges from low micromolar to ~0.3 µM for optimized analogs.^27,28

Medicinal chemistry insight

Chalcones are ideal for rational scaffold extension into the acyl-binding channel.²⁸

Figure 2: Representative pancreatic lipase inhibitor structures. (A) Orlistat, the only FDA-approved inhibitor (IC50 = 0.092 μM). (B) EGCG-digallate, the most potent natural product (IC50 = 0.098 μM).      Click here to view Figure

Synthetic Scaffolds — Where Medicinal Chemistry Truly Begins

Thiazolidinedione (TZD) (Reversible Covalent Mimics without the Risks)

TZDs mimic the orientation of the catalytic serine’s transition state but do so reversibly.²⁹ They engage the catalytic pocket by:

Acting as hydrogen-bond acceptors.⁴

Presenting large, polarizable aromatic “faces” to Phe77.^4,30

Aligning substituents to occupy the hydrophobic channel.³⁰

Potency

Often 2–10 µM, with optimized derivatives approaching submicromolar.²⁹

Quinazolinone–Coumarin Hybrids — Dual-Action Precision

These scaffolds introduce:

A quinazolinone core for hydrogen bonding near Ser152

A coumarin chromophore for aromatic stacking.³¹

Halogenated benzyl groups providing hydrophobic anchoring.³²

Unique advantage: simultaneous lipase + α-glucosidase inhibition — ideal for metabolic syndrome.³²

Triazoles — Small, Cheap, Stable, and Surprisingly Bioactive

Triazoles

Offer predictable synthetic access (CuAAC “click” chemistry).³³

Provide heteroatom-rich surfaces for hydrogen bonding.³⁴

Support bulky aromatic groups that fit the hydrophobic tunnel.³⁵

Potency

Low micromolar (1–5 µM) for optimized variants.³⁶

Unified Structure-Activity Principles Across Inhibitor Classes

Comparative analysis of all inhibitor classes reveals convergent SAR principles that define pancreatic lipase binding affinity:

Hydrophobic Tunnel Engagement

The acyl-binding channel accommodates linear chains of C10-C15 optimal length.^4,17 β-Lactones exploit this with saturated fatty acid mimics, while aurones and chalcones achieve equivalent occupancy through extended alkoxy substituents (C6-C10).^24,25,27 Chains shorter than C6 lose tunnel contacts; chains beyond C16 induce steric clashes with Ile209 and Val260.⁴

Aromatic Platform Interactions

Phe77 and Tyr114 form a π-stacking platform essential for inhibitor orientation.²¹ Flavonoids position their B-ring against this surface, TZDs present thiazolidinedione aromatic faces, and triazoles dock heterocyclic cores.Compounds lacking planar aromatic systems show 10-50 fold potency loss regardless of other favorable features.^37,38

Catalytic Triad Recognition

Hydrogen bonding to Ser152 (directly or via water-mediated contacts) and His263 stabilizes the binding pose.³⁸ The oxyanion hole (Phe77, Leu153 backbone NHs) provides additional anchoring for carbonyl or hydroxyl acceptors.EGCG derivatives achieve sub-micromolar potency through simultaneous engagement of all three interaction nodes.^24,39

Conformational Compatibility

Rigid, planar scaffolds (aurones, chalcones, quinazolinones) maintain pre-organized conformations that minimize entropic penalties upon binding. Flexible inhibitors pay conformational costs that reduce effective binding free energy by 1-2 kcal/mol.

The integration of these four principles—rather than optimization of any single feature—distinguishes sub-micromolar inhibitors from weak binders across all chemical classes.

Table 1: provides a comprehensive comparison of representative inhibitors across all chemical classes, highlighting IC₅₀ values, mechanisms of action, and key structural determinants of potency.”

Table 1: Representative Pancreatic Lipase Inhibitors

Computational Approaches

Computational tools are uniquely powerful for lipase because multiple high-resolution structures exist in both “open” and “closed” conformations. The enzyme’s deep, solvophobic pocket, its lid mobility, and the colipase interaction surface create a complex landscape ideally suited for in silico exploration.

Docking — What It Actually Reveals

Effective docking reveals:³⁷

Whether the compound can position a hydrophobic chain parallel to the acyl-binding tunnel.⁴

If its polar motifs can engage Ser152, His263, or oxyanion-hole NHs^1,42

How well its aromatics align with Phe77 / Tyr114^24,42

Whether bulky substituents clash with the lid domain.⁴

Docking consistently highlights a “triad interaction cluster” — the positioning of the ligand relative to Ser152, His263, and Phe77 predicts 60–80% of potency variance.

Molecular Dynamics (MD)— Capturing the Enzyme’s Real Behavior

MD reveals:⁴³

Lid flexibility over 50–200 ns⁴⁴

Stability of hydrogen bonds (Ser152/His263 occupancy %)^4,44

Water molecules mediating key contacts⁴⁵

Shape complementarity changes

Whether bulky ligands distort the tunnel (undesirable)

For potent ligand–enzyme complexes:

Root mean square deviation (RMSD): <3 ų⁹

Root mean square fluctuation (RMSF) (catalytic residues): <1.5 ų⁹

H-bond occupancy: >50%⁴⁵

Hydrophobic tunnel remains undisturbed⁴

Quantitative Structure-Activity Relationship (QSAR) — What Actually Improves Potency

Across published QSAR models, three descriptor clusters consistently correlate with PL inhibition:

Hydrophobicity

Lipophilic interactions explain why C10–C15 chains outperform others.⁴⁶

Planarity & rigidity⁴⁷

Favorable for π–π stacking and entering the narrow tunnel.

H-bond acceptor count

Essential for catalytic triad engagement.^38,46

Revised rule-of-thumb:^38,47
LogP ~3–6 + planar core + one HBA near Ser152⁴² → good starting point.

Pharmacophore Models — The “Minimum Binding Requirements”

Consensus pharmacophore typically includes:

1 Hydrogen bond acceptor (HBA) near Ser152.^1,42

Two hydrophobic sites along the tunnel^1,4

One aromatic ring for stacking⁴²

Optional HBD for oxyanion hole stabilization

Any scaffold lacking the hydrophobic pair almost always shows weak activity.^4,48

Artificial Intelligence (AI) & Fragment-Based Drug Design (FBDD) — Modern Tools for a Traditionally Overlooked Target

Machine learning (ML) models rapidly predict lipase inhibitors with >80% classification accuracy.^47,49

Generative deep-learning models can propose non–β-lactone scaffolds optimized for potency + GI confinement.^50,51

FBDD identifies small tunnel-binding fragments that can be linked or merged to produce potent, lipophilic inhibitors without covalent warheads.⁵²

AI offers a path to lipase inhibitors without the problems of orlistat, a previously unthinkable direction.

Table 2 summarizes validation metrics from key computational studies, demonstrating strong correlations between in silico predictions and experimental potency data, validating these methodologies for prospective inhibitor design.

Table 2: Computational Validation Metrics

Comparative analysis of irreversible versus reversible inhibition strategies reveals distinct trade-offs between potency, tolerability, and therapeutic flexibility (Table 3). While β-lactones achieve superior enzymatic inhibition, reversible scaffolds offer advantages in safety profiles and mechanistic versatility that may prove decisive for next-generation therapeutics.

Table 3: β-Lactone vs. Reversible Inhibitors – Comparative Profile

Future Medicinal Chemistry Strategies

Allosteric and Partial Inhibition (“Smart Inhibition”)

Instead of knocking out lipase activity entirely, future inhibitors aim for controlled attenuation:

40–70% inhibition.⁵⁴

Minimal GI side effects

Preserved gastrointestinal physiology

Improved adherence

Reduced compensatory overeating

Targeting the lid hinge, colipase interface, or surface loops provides viable allosteric entry points.^4,54,58,59

Multi-Target Chemotypes — Metabolic Network Thinking

Obesity is not a single-pathway disease. Hybrid molecules that act on:^32,57

PL + α-glucosidase.³²

PL + peroxisome proliferator-activated receptor gamma (PPARγ).⁵⁷

PL + AMP-activated protein kinase (AMPK)⁶⁰

can modulate both fat and carbohydrate absorption while influencing systemic metabolism.

Dual-modulators = lower doses, fewer side effects, greater metabolic impact.⁶¹

Beyond β-Lactones — Safe Covalent and Non-Covalent Alternatives

Medicinal chemists are now exploring:

Boronic acids (reversible covalent, tunable)^62,63

Phenyl carbamates (stable, non-strained electrophiles)^64,65

Soft electrophiles activated only near Ser152.⁶⁶

These promise potency without the toxicity of classic β-lactones.^62,64

Bridging the Translational Gap: Why Potent In Vitro Inhibitors Fail Clinically

Despite numerous inhibitors demonstrating sub-micromolar potency in enzymatic assays, only orlistat has achieved clinical approval, revealing critical translational barriers.³ Potent natural products like galloylated catechins (IC₅₀ 0.048 μM) undergo extensive degradation in gastric acid, rapid enterocyte metabolism, and oxidative instability under intestinal conditions, dramatically reducing effective concentrations at the lipid-water interface.^23,56Additionally, complete lipase inhibition triggers compensatory physiological responses—pancreatic enzyme hypersecretion, altered bile acid synthesis, and microbiome-mediated fat metabolism—explaining why 95% enzymatic inhibition translates to only 30% reduction in fat absorption clinically.² Overcoming these barriers requires integrated preclinical models incorporating simulated intestinal conditions and mechanistic biomarkers that predict clinical fat malabsorption beyond simple IC₅₀ values.

Targeting the Lipophilic Tunnel — The Real Key to Potency

New scaffolds explicitly aim to:

Extend into the hydrophobic tunnel.¹

Position aromatic or aliphatic substituents to fill “hot spots”.⁴

Exploit tunnel residues (Ile209, Leu213, Val260, Phe215).⁶⁷

This is the feature β-lactones exploit — medicinal chemists can mimic it without irreversible chemistry.³

Designing GI-Localized Drugs

Best-in-class PL inhibitors will be:

Highly lipophilic (LogP > 5)⁵⁵

High molecular weight⁵⁵

Poorly permeable^55,56

Designed for local GI action⁵⁶

Cleared almost exclusively in feces^56,68,69

This pharmacokinetic design is a feature, not a flaw — the exact opposite of classical drug design.

Discussions

This comprehensive review synthesizes mechanistic, structural, and medicinal chemistry perspectives on pancreatic lipase inhibition, revealing both the molecular basis for current therapeutic limitations and the strategic pathways toward improved inhibitor design.

From Mechanism to Medicinal Chemistry: Integrating Biological Insights

The interfacial activation mechanism of pancreatic lipase—unique among digestive enzymes—presents both opportunities and challenges for inhibitor development.^1,7 Unlike soluble enzymes where active-site geometry alone dictates binding, lipase requires inhibitors to function at lipid-water interfaces, engage dynamically with the lid domain, and compete with physiological substrates organized in mixed micelles. This explains why classical structure-activity relationship predictions often fail: compounds optimized for aqueous binding may not maintain potency under interfacial conditions.⁸

The catalytic triad (Ser152-His263-Asp176) and oxyanion hole provide well-defined molecular targets,^8,9 yet β-lactone inhibitors—despite near-perfect transition-state mimicry^17,18 suffer from irreversible chemistry that triggers compensatory physiological responses.² This potency-tolerability paradox underscores a critical lesson: maximal enzyme inhibition does not translate to optimal therapeutic outcomes. The future lies in partial, tunable inhibition (40-70% activity reduction)⁵⁴ that preserves basal lipid digestion while reducing caloric absorption.

Reversible Inhibitors as Safer Alternatives: Balancing Potency and Physiological Compatibility

Natural products, particularly galloylated catechins (IC₅₀ 0.048 μM) and optimized chalcones (IC₅₀ 0.33 μM),²⁷ demonstrate that reversible, non-covalent scaffolds can achieve potencies rivaling orlistat when designed for optimal hydrophobic tunnel occupancy and aromatic platform engagement.^24,25 However, their clinical translation is hindered by metabolic instability, pH-dependent degradation, and poor formulation characteristics.^23,56

Synthetic scaffolds—thiazolidinediones,^29,30 triazoles,^34–36 quinazolinone-coumarin hybrids³² address these limitations through enhanced chemical stability and tunable pharmacokinetic properties. The quinazolinone-coumarin dual inhibitors represent a particularly promising strategy: simultaneous modulation of pancreatic lipase and α-glucosidase addresses both fat and carbohydrate absorption, potentially offering superior metabolic control with lower individual enzyme inhibition requirements.

Computational Tools: From Predictive Limitations to Strategic Utility

Molecular docking and dynamics simulations have proven invaluable for understanding binding modes and identifying key interaction hotspots (Ser152, His263, Phe77, Tyr114).^37,42,70 However, their predictive accuracy for interfacial enzymes remains constrained by the inability to model lipid-water boundaries, bile salt effects, and colipase-mediated conformational changes. Computational tools should thus be viewed as hypothesis-generating and mechanistic-interrogation instruments rather than definitive predictors of in vivo efficacy.

QSAR models consistently identify lipophilicity (LogP 3-6), planarity, and hydrogen-bond acceptor capacity as dominant determinants of potency,^46,47 but this risks reinforcing lipophilic bias that contributes to poor bioavailability and formulation challenges. Machine-learning and AI-driven scaffold generation^50–52 offer opportunities to escape this local chemical space, potentially identifying novel non-β-lactone frameworks optimized for both potency and drug-like properties.

Translational Barriers: Why In Vitro Potency Fails Clinically

The persistent disconnect between enzymatic potency and clinical efficacy reflects fundamental misalignments between experimental models and physiological reality.^3,56 Simplified assays using p-nitrophenyl substrates bypass interfacial activation requirements, while in vivo fat digestion involves bile salt competition, colipase stabilization, and complex micellar organization.^1,8 Furthermore, complete lipase inhibition triggers adaptive responses—pancreatic enzyme hypersecretion, altered bile acid synthesis, microbiome-mediated compensatory pathways—that diminish therapeutic impact despite sustained enzymatic blockade.²

Successful next-generation inhibitors must be designed with these physiological constraints in mind: GI-localized pharmacokinetics (high MW, extreme lipophilicity, poor permeability^55,56) to minimize systemic exposure; partial inhibition profiles to avoid triggering compensatory mechanisms; and multi-target activity to address metabolic dysfunction comprehensively rather than through single-enzyme modulation.

Strategic Priorities for Future Inhibitor Development

Four strategic directions emerge from this integrated analysis:

Allosteric and partial inhibition: Targeting the lid-colipase interface or surface loops^54,58,71 to achieve controllable, tunable activity reduction without complete active-site blockade.

Multi-target hybrid molecules: Dual PL/α-glucosidase³² or PL/PPARγ⁵⁷ inhibitors that address multiple metabolic pathways simultaneously, potentially reducing required doses and side-effect burden.

Non-β-lactone electrophiles: Boronic acids, carbamates, and sulfonyl fluorides^62–66 offer reversible or slowly-reversible covalent mechanisms with reduced toxicity profiles compared to classical β-lactones.

GI-restricted pharmacokinetics: Deliberate design for intestinal localization^55,56 through high molecular weight, extreme lipophilicity, and poor permeability—converting traditional “drug-likeness” liabilities into therapeutic assets.

The ultimate goal is not to replicate orlistat’s potency but to transcend its limitations: achieving meaningful fat malabsorption with improved tolerability, adherence, and long-term sustainability. This requires embracing partial inhibition, allosteric modulation, and multi-target strategies that align molecular design with physiological reality.

Conclusion

Pancreatic lipase represents a structurally well-characterized hydrolytic enzyme whose inhibition offers rational obesity management through reduced dietary fat absorption. However, clinical success has been limited by orlistat’s potency-tolerability paradox: irreversible covalent inhibition triggers gastrointestinal side effects and compensatory physiological responses that diminish long-term efficacy.

This review demonstrates that future progress lies not in replicating orlistat’s mechanism but in transcending it through innovative medicinal chemistry. Reversible natural products—galloylated catechins, aurones, and chalcones—achieve sub-micromolar potencies when optimized for hydrophobic tunnel engagement and catalytic triad recognition. Synthetic scaffolds including thiazolidinediones, triazoles, and quinazolinone-coumarin hybrids extend this principle through enhanced stability, tunable pharmacokinetics, and multi-target capabilities.

Computational approaches combining molecular dynamics, QSAR modeling, and AI-driven scaffold generation provide mechanistic insights that accelerate rational inhibitor development, though interfacial enzyme behavior remains incompletely modeled.

Four strategic priorities define next-generation development: allosteric and partial inhibition targeting the lid-colipase axis; multi-target hybrids addressing both lipid and carbohydrate metabolism; non-β-lactone electrophiles offering safer covalent mechanisms; and GI-restricted pharmacokinetics through deliberate high molecular weight design.

Success requires embracing partial rather than maximal inhibition, allosteric rather than purely active-site targeting, and multi-target rather than single-enzyme modulation. By aligning molecular design with pancreatic lipase’s unique interfacial activation mechanism, next-generation inhibitors can achieve meaningful clinical impact while avoiding orlistat’s tolerability limitations

Acknowledgement

The authors gratefully acknowledge the Almighty for guidance and strength, their families for unwavering support and, Gujarat Technological University, Ahmedabad, for providing the necessary institutional support and facilities that enabled the successful completion of this work. 

Funding Sources

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Conflict of Interest

The authors do not have any conflict of interest.

Data Availability Statement

This statement does not apply to this article.

Ethics Statement

This research did not involve human participants, animal subjects, or any material that requires ethical approval.

Informed Consent Statement

This study did not involve human participants, and therefore, informed consent was not required.

Clinical Trial Registration

This research does not involve any clinical trials.

Permission to reproduce material from other sources

Not Applicable 

Author Contributions

Neil Bienkumar Panchal: Conceptualization, Literature review, Data curation, Writing – Original draft, Visualization, Validation.

Vipul Manusinh Vaghela: Supervision, Writing – Review & editing, Project administration, Final approval. All authors have read and approved the final manuscript for publication.

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Abbreviations

AI – Artificial Intelligence

AMPK – AMP-Activated Protein Kinase

EGCG – Epigallocatechin Gallate

FBDD – Fragment-Based Drug Design

GI – Gastrointestinal

MD – Molecular Dynamics

MM-GBSA – Molecular Mechanics Generalized Born Surface Area

MM-PBSA – Molecular Mechanics Poisson-Boltzmann Surface Area

PDB – Protein Data Bank

PL – Pancreatic Lipase

PPARγ – Peroxisome Proliferator-Activated Receptor Gamma

QSAR – Quantitative Structure-Activity Relationship

RMSD – Root Mean Square Deviation

RMSF – Root Mean Square Fluctuation

SAR – Structure-Activity Relationship

TZD – Thiazolidinedione