CRCM Journal

Introduction

For decades, a small repertoire of drug classes—beta blockers, ACE inhibitors, angiotensin receptor blockers (ARBs), calcium channel blockers (CCBs), statins, and select diuretics—has underpinned cardiovascular disease (CVD) management. These agents are cost effective, well-studied, and remain first line therapy in hypertension, heart failure, ischemic heart disease, and arrhythmias [1-3].

Until recently, pharmacology of these agents was framed in terms of single target effects—e.g., β₁ adrenergic antagonism, RAAS inhibition, or L type calcium channel blockade. However, in an era of multi omic profiling and network medicine, this reductionist view is obsolete. Instead, these classical agents exhibit polypharmacology—modulating multiple targets and pathways, often via subtle off target or indirect effects that manifest at the network level [3-10]. This enables mechanistic redundancy, providing therapeutic robustness against patient variability and disease complexity.

Systems pharmacology, one of the principles of polypharmacology [7]—the melding of systems biology with pharmacological data— offers a framework to formally analyze these network effects. It leverages transcriptomics, proteomics, metabolomics, and computational modeling to map drug–target–disease interactions.

This review offers (1) a systems level reinterpretation of classical cardiovascular drugs, (2) an exploration of mechanistic redundancy as a therapeutic asset, (3) repurposing opportunities unveiled by network analysis and omics data, and (4) implications for the design of next generation polypharmacological agents.

Systems Pharmacology and Network Pharmacology in Cardiovascular Medicine

Systems pharmacology applies systems biology principles to drug action, emphasizing drug–target networks rather than single interactions. It integrates drug target data, protein protein and gene disease networks, and omics responses to understand drug effects across scales—from molecular to organ system.

A landmark network pharmacology mapping of 254 FDA approved cardiovascular drugs and their 206 known targets revealed an average of ~2.8 targets per drug, with some agents like verapamil and dronedarone engaging over a dozen targets [10]. This confirms widespread drug promiscuity, which in cardiovascular agents often proves beneficial rather than adverse.

Network medicine further frames disease as clusters of interacting genes and pathways. Drugs that modulate multiple nodes in disease networks—rather than single points—may deliver greater efficacy in complex conditions such as heart failure or hypertension, which are marked by redundancy and feedback loops [7,11].

Omics profiling (transcriptomics, proteomics, metabolomics, epigenomics) complements network modeling. For instance, transcriptomic analyses demonstrate that beta blockers modulate gene sets involved in mitochondrial function, oxidative stress, and fibrosis. Proteomics shows ACE inhibitors influence kinase and extracellular matrix pathways beyond RAAS; metabolomics reveals how statins alter bioenergetic and inflammatory metabolites.

Such integrative approaches form the backbone of network pharmacology in cardiovascular research—as documented in multiple reviews focused on both drug discovery and therapeutic repurposing [7,12].

Reinterpreting Classical Cardiovascular Agents

Beta Blockers

Initially targeting β₁ adrenergic receptors to reduce heart rate and myocardial oxygen demand, beta blockers also exhibit anti inflammatory, anti fibrotic, anti arrhythmic, and endocrine modulating effects. Network pharmacology and omics analyses reveal modulation of calcium handling, oxidative phosphorylation, fibrosis related genes, and microRNA expression. For example, carvedilol, beyond β blockade, acts as an antioxidant and inhibits lipid peroxidation, likely via direct radical scavenging and modulation of mitochondrial gene networks (Figure 1).

Beta blockers reduce renin secretion and modulate the RAAS indirectly; they also attenuate stress hormone mediated pathways involved in angiogenesis and inflammation—effects implicated in observed associations with reduced cancer progression.

ACE Inhibitors and ARBs

Beyond inhibiting angiotensin converting enzyme (ACE) or blocking AT₁ receptors, these agents enhance bradykinin and nitric oxide signaling, and modulate extracellular matrix remodeling via MMPs and collagen pathways. Network studies and proteomics mapping show ACE inhibitors alter endothelial kinase pathways and inflammation mediators. Telmisartan, an ARB, also activates PPAR γ, linking it to metabolic regulation and insulin sensitivity— disclosed through systems level analysis.

In heart failure, ACE inhibitors reduce adverse remodeling and fibrosis through pleiotropic signaling beyond classical RAAS blockade and confer endothelial protection via network level cross talk among nitric oxide, TGF β, and inflammatory axes (Figure 1).

Statins

While statins primarily inhibit HMG CoA reductase, metabolomics and transcriptomics have revealed robust anti inflammatory and endothelial protective effects (Figure 1). They downregulate NF κB signaling networks, enhance eNOS activity, stabilize plaques, reduce oxidative stress, and modulate immune cell transcriptional programs. These pleiotropic, polypharmacologic actions help explain clinical benefits of statins even in normocholesterolemic patients.

Calcium Channel Blockers (CCBs)

CCBs like amlodipine, verapamil, and diltiazem target L type calcium channels to reduce vascular resistance. Network analyses, however, indicate broader effects on mitochondrial function, ROS pathways, and even neuronal circuits regulating autonomic tone. Omics studies show modulation of signaling pathways in vascular smooth muscle beyond calcium influx, suggesting secondary network mechanisms contributing to benefits in ischemia and hypertension (Figure 1).

Figure 1: Hidden Polypharmacology of Classical Cardiovascular Drugs.

This systems pharmacology network illustrates the multitarget actions of classical cardiovascular drug classes—beta-blockers, ACE inhibitors/ARBs, statins, and calcium channel blockers (CCBs). Each drug class, originally developed for a single primary target (e.g., β₁-adrenergic receptors, HMG-CoA reductase), also modulates additional pathways including inflammation, oxidative stress, mitochondrial function, and bradykinin/nitric oxide (NO) signaling. The surrounding blue ring highlights the contribution of multi-omics approaches (genomics, transcriptomics, proteomics, and metabolomics) in uncovering these hidden effects. The figure underscores the emergent concept of therapeutic redundancy and polypharmacology revealed through systems-level analyses.

Mechanistic Redundancy as a Therapeutic Strength

Mechanistic redundancy—multiple targets converging on similar disease-relevant endpoints—was once considered pharmacologic clutter. Systems pharmacology repositions redundancy as therapeutic robustness. In multifactorial disease networks, redundant mechanisms enhance resilience to inter individual variability, genetic polymorphisms, and compensatory pathways [7].

Beta blockers, ACE inhibitors, and statins each impact overlapping pathways—RAAS, sympathetic signaling, inflammation, endothelial function, fibrosis—offering redundant suppression of pathogenic circuits. This reduces reliance on any single pathway and increases durability of response, even when disease biology shifts (e.g. heart failure progression) [13-15].

Moreover, redundancy reduces emergence of resistance: mono target agents may lose efficacy through upregulation of alternative routes, whereas polypharmacological drugs deliver network coverage that maintains benefit despite partial escape.

Repurposing Old Drugs Using Systems Pharmacology

Systems driven polypharmacology has uncovered repurposing potential for classical agents [7,8]:

• Beta blockers in cancer: Some epidemiologic data suggest reduced tumor progression, possibly via modulation of β adrenergic stress signalling impacting angiogenesis and invasion.
• Statins in neurodegeneration: Via anti-inflammatory and cholesterol independent network effects, statins may modulate Alzheimer’s related pathways.
• ACE inhibitors and ARBs in metabolic disorders: Mechanistic links through PPAR-γ and bradykinin nitric oxide networks suggest benefits in diabetes and insulin resistance.

These associations emerge when mapping drug targets onto disease protein networks and overlaying omics signatures, providing computational hypotheses for clinical repurposing studies.

Lessons for Next Generation Polypharmacological Drug Design

What cardiovascular systems pharmacology teaches us:

1. Design for polypharmacology: Rather than hyper selective molecules, network informed drugs that modulate multiple disease relevant nodes may yield higher efficacy in complex syndromes such as HFpEF, cardiorenal or cardio metabolic disease [7,8].
2. Use omics as drug fingerprints: Profiling transcriptomic, proteomic, and metabolomic responses of classical drugs provides templates for synthetic analogs or combinations aimed at recapitulating beneficial network signatures [16-19].
3. Integrate AI and systems modeling: Machine learning on drug target disease networks can predict multi target effects across patient genotypes and phenotypes, enabling more precise, personalized polypharmacology.
4. Explore rational drug combinations: Fixed or co formulated regimens—e.g. combining beta blockers, ACE inhibitors, and statins—may deliver expanded, synergistic network coverage with reduced toxicity [13-15].

These strategies align with broader trends in quantitative systems pharmacology and network medicine approaches to drug discovery and therapeutic optimization.

Challenges and Future Directions

Despite the transformative promise of systems pharmacology and polypharmacology, several key challenges must be addressed to realize their full clinical potential:

• Data integration: Multi-omics datasets are often noisy, context-dependent, and lack standardization [16-19]. This variability complicates accurate network reconstruction and limits the reproducibility of systems pharmacology models across studies and patient populations.
• Clinical validation: While in silico predictions and omicsbased insights are abundant, few multitarget hypotheses have been robustly validated in large-scale clinical trials. Translational bottlenecks remain, particularly for repurposing old drugs based on network-level rationale.
• Regulatory and ethical frameworks: Current drug approval processes are still largely geared toward single-target drugs with clearly defined mechanisms. Multitarget drugs—especially those developed through systems approaches or for off-label indications—require new regulatory paradigms to accommodate complex evidence bases, network models, and polypharmacologic profiles.
• Precision variability: Genetic, epigenetic, and environmental factors shape individual drug responses and network dynamics. Systems pharmacology must increasingly integrate patient-level data to enable precision polypharmacology, ideally through personalized network models.

To overcome these challenges, future efforts should prioritize integrative platforms that combine deep phenotyping, multiomic profiling, machine learning, and real-world outcome data. Emerging technologies such as digital twins, multiscale computational models of cardiovascular physiology, and AIguided network therapeutics offer promising tools to optimize drug selection, dosing, and design in a personalized and dynamic manner.

Moreover, while AI-assisted drug discovery has significantly accelerated target identification and compound screening, discovering small molecules that engage multiple targets with favorable pharmacokinetics, low toxicity, and appropriate selectivity remains a largely stochastic and resource-intensive endeavor.

In this context, the emergence of nucleic acid-based therapeutics (NATs)—such as siRNA, antisense oligonucleotides (ASOs), mRNA, and microRNA modulators—has made the discovery and development of multitarget drugs (MTDs) significantly more feasible, programmable, and precise than with traditional smallmolecule agents [20-31] (Figure 2). This advantage is grounded in several key features:

1. Intrinsic sequence programmability: NBTs are defined by their nucleotide sequence rather than by rigid chemical structures. This enables deliberate design of multitarget strategies by:

• Creating siRNA cocktails or multiplexed ASOs that silence several genes simultaneously;
• Engineering microRNA mimics or inhibitors that naturally regulate networks of targets [20,23-25,30,31]; 
• Encoding multiple protein domains using multi-cistronic mRNA constructs.