Optimal drug cocktail design: methods for targeting molecular ensembles and insights from theoretical model systems

Mono- and combinational drug therapies for global viral pandemic preparedness

Broadly effective antiviral therapies must be developed to be ready for clinical trials, which should begin soon after the emergence of new life-threatening viruses. Here, we pave the way towards this goal by reviewing conserved druggable virus-host interactions, mechanisms of action, immunomodulatory properties of available broad-spectrum antivirals (BSAs), routes of BSA delivery, and interactions of BSAs with other antivirals. Based on the review, we concluded that the range of indications of BSAs can be expanded, and new pan- and cross-viral mono- and combinational therapies can be developed. We have also developed a new scoring algorithm that can help identify the most promising few of the thousands of potential BSAs and BSA-containing drug cocktails (BCCs) to prioritize their development during the critical period between the identification of a new virus and the development of virus-specific vaccines, drugs, and therapeutic antibodies.

Action Mechanism Underlying Improvement Effect of Fuzi Lizhong Decoction on Nonalcoholic Fatty Liver Disease: A Study Based on Network Pharmacology and Molecular Docking

OBJECTIVE

This study aimed to decipher the bioactive compounds and potential mechanism of traditional Chinese medicine (TCM) formula Fuzi Lizhong Decoction (FLD) for nonalcoholic fatty liver disease (NAFLD) treatment via an integrative network pharmacology approach.

METHODS

The candidate compounds of FLD and its relative targets were obtained from the TCMSP and PharmMapper web server, and the intersection genes for NAFLD were discerned using OMIM, GeneCards, and DisGeNET. Then, the PPI and component-target-pathway networks were constructed. Moreover, GO enrichment and KEGG pathway analysis were performed to investigate the potential signaling pathways associated with FLD's effect on NAFLD. Eventually, molecular docking simulation was carried out to validate the binding affinity between potential core components and key targets.

RESULTS

A total of 143 candidate active compounds and 129 relative drug targets were obtained, in which 61 targets were overlapped with NAFLD. The PPI network analysis identified ALB, MAPK1, CASP3, MARK8, and AR as key targets, mainly focusing on cellular response to organic cyclic compound, steroid metabolic process, and response to steroid hormone in the biological processes. The KEGG pathway analysis demonstrated that 16 signaling pathways were closely correlated with FLD's effect on NALFD with cancer pathways, Th17 cell differentiation, and IL-17 signaling pathways as the most significant ones. In addition, the molecular docking analysis revealed that the core active compounds of FLD, such as 3'-methoxyglabridin, chrysanthemaxanthin, and Gancaonin H, had a high binding activity with such key targets as ALB, MAPK1, and CASP3.

CONCLUSIONS

This study suggested that FLD exerted its effect on NAFLD via modulating multitargets with multicompounds through multipathways. It also demonstrated that the network pharmacology-based approach might provide insights for understanding the interrelationship between complex diseases and interventions of the TCM formula.

Natural Products in Modulating Methamphetamine-Induced Neuronal Apoptosis

Methamphetamine (METH), an amphetamine-type psychostimulant, is highly abused worldwide. Chronic abuse of METH causes neurodegenerative changes in central dopaminergic neurons with numerous neuropsychiatric consequences. Neuronal apoptosis plays a critical role in METH-induced neurotoxicity and may provide promising pharmacological targets for preventing and treating METH addiction. In recent years, accumulating evidence has revealed that natural products may possess significant potentials to inhibit METH-evoked neuronal apoptosis. In this review, we summarized and analyzed the improvement effect of natural products on METH-induced neuronal apoptosis and their potential molecular mechanisms on modulating dopamine release, oxidative stress, mitochondrial-dependent apoptotic pathway, endoplasmic reticulum stress-mediated apoptotic pathway, and neuroinflammation. Hopefully, this review may highlight the potential value of natural products in modulating METH-caused neuronal apoptosis and provide useful information for future research and developments of novel and efficacious pharmacotherapies in this field.

Significance of Brain Glucose Hypometabolism, Altered Insulin Signal Transduction, and Insulin Resistance in Several Neurological Diseases

Several neurological diseases share pathological alterations, even though they differ in their etiology. Neuroinflammation, altered brain glucose metabolism, oxidative stress, mitochondrial dysfunction and amyloidosis are biological events found in those neurological disorders. Altered insulin-mediated signaling and brain glucose hypometabolism are characteristic signs observed in the brains of patients with certain neurological diseases, but also others such as type 2 diabetes mellitus and vascular diseases. Thus, significant reductions in insulin receptor autophosphorylation and Akt kinase activity, and increased GSK-3 activity and insulin resistance, have been reported in these neurological diseases as contributing to the decline in cognitive function. Supporting this relationship is the fact that nasal and hippocampal insulin administration has been found to improve cognitive function. Additionally, brain glucose hypometabolism precedes the unmistakable clinical manifestations of some of these diseases by years, which may become a useful early biomarker. Deficiencies in the major pathways of oxidative energy metabolism have been reported in patients with several of these neurological diseases, which supports the hypothesis of their metabolic background. This review remarks on the significance of insulin and brain glucose metabolism alterations as keystone common pathogenic substrates for certain neurological diseases, highlighting new potential targets.

Controlled release of artemisone for the treatment of experimental cerebral malaria

Background

Cerebral malaria (CM) is a leading cause of malarial mortality resulting from infection by Plasmodium falciparum. Treatment commonly involves adjunctive care and injections or transfusion of artemisinins. All artemisinins that are in current use are metabolized to dihydroxyartemisinin (DHA), to which there is already some parasite resistance. We used artemisone, a derivative that does not convert to DHA, has improved pharmacokinetics and anti-plasmodial activity and is also anti-inflammatory (an advantage given the immunopathological nature of CM).

Methods

We examined controlled artemisone release from biodegradable polymers in a mouse CM model. This would improve treatment by exposing the parasites for a longer period to a non-toxic drug concentration, high enough to eliminate the pathogen and prevent CM. The preparations were inserted into mice as prophylaxis, early or late treatment in the disease course.

Results

The most efficient formulation was a rigid polymer, containing 80 mg/kg artemisone, which cured all of the mice when used as early treatment and 60% of the mice when used as a very late treatment (at which stage all control mice would die of CM within 24 h). In those mice that were not completely cured, relapse followed a latent period of more than seven days. Prophylactic treatment four days prior to the infection prevented CM. We also measured the amount of artemisone released from the rigid polymers using a bioassay with cultured P. falciparum. Significant amounts of artemisone were released throughout at least ten days, in line with the in vivo prophylactic results.

Conclusions

Overall, we demonstrate, as a proof-of-concept, a controlled-sustained release system of artemisone for treatment of CM. Mice were cured or if treated at a very late stage of the disease, depicted a delay of a week before death. This delay would enable a considerable time window for exact diagnosis and appropriate additional treatment. Identical methods could be used for other parasites that are sensitive to artemisinins (e.g. Toxoplasma gondii and Neospora caninum).

Unlocking Nanocarriers for the Programmed Release of Antimalarial Drugs

A programmable release system with wide range of release profiles of the antimalarial artemisone (ART) from fibrous nanocarriers (NFN) is presented. This is achieved following a new paradigm of using ART-loaded NFN in infusion system of hydrophobic drug eluting nanocarriers, adapted to clinical applications. Very importantly, under these conditions ART did not degrade as it was observed in solution.

Computational Multitarget Drug Design

Designing drugs that can simultaneously interact with multiple targets is a promising approach for treating complicated diseases. Compared to using combinations of single target drugs, multitarget drugs have advantages of higher efficacy, improved safety profile, and simpler administration. Many in silico methods have been developed to approach different aspects of this polypharmacology-guided drug design, particularly for drug repurposing and multitarget drug design. In this review, we summarize recent progress in computational multitarget drug design and discuss their advantages and limitations. Perspectives for future drug development will also be discussed.

Differential selective pressure alters rate of drug resistance acquisition in heterogeneous tumor populations

Recent drug discovery and development efforts have created a large arsenal of targeted and chemotherapeutic drugs for precision medicine. However, drug resistance remains a major challenge as minor pre-existing resistant subpopulations are often found to be enriched at relapse. Current drug design has been heavily focused on initial efficacy, and we do not fully understand the effects of drug selective pressure on long-term drug resistance potential. Using a minimal two-population model, taking into account subpopulation proportions and growth/kill rates, we modeled long-term drug treatment and performed parameter sweeps to analyze the effects of each parameter on therapeutic efficacy. We found that drugs with the same overall initial kill may exert differential selective pressures, affecting long-term therapeutic outcome. We validated our conclusions experimentally using a preclinical model of Burkitt's lymphoma. Furthermore, we highlighted an intrinsic tradeoff between drug-imposed overall selective pressure and rate of adaptation. A principled approach in understanding the effects of distinct drug selective pressures on short-term and long-term tumor response enables better design of therapeutics that ultimately minimize relapse.

Modeling Tumor Clonal Evolution for Drug Combinations Design

Cancer is a clonal evolutionary process. This presents challenges for effective therapeutic intervention, given the constant selective pressure towards drug resistance. Mathematical modeling from population genetics, evolutionary dynamics, and engineering perspectives are being increasingly employed to study tumor progression, intratumoral heterogeneity, drug resistance, and rational drug scheduling and combinations design. In this review, we discuss promising opportunities these inter-disciplinary approaches hold for advances in cancer biology and treatment. We propose that quantitative modeling perspectives can complement emerging experimental technologies to facilitate enhanced understanding of disease progression and improved capabilities for therapeutic drug regimen designs.

Molecular mechanisms and design principles for promiscuous inhibitors to avoid drug resistance: lessons learned from HIV-1 protease inhibition

Molecular recognition is central to biology and ranges from highly selective to broadly promiscuous. The ability to modulate specificity at will is particularly important for drug development, and discovery of mechanisms contributing to binding specificity is crucial for our basic understanding of biology and for applications in health care. In this study, we used computational molecular design to create a large dataset of diverse small molecules with a range of binding specificities. We then performed structural, energetic, and statistical analysis on the dataset to study molecular mechanisms of achieving specificity goals. The work was done in the context of HIV‐1 protease inhibition and the molecular designs targeted a panel of wild‐type and drug‐resistant mutant HIV‐1 protease structures. The analysis focused on mechanisms for promiscuous binding to bind robustly even to resistance mutants. Broadly binding inhibitors tended to be smaller in size, more flexible in chemical structure, and more hydrophobic in nature compared to highly selective ones. Furthermore, structural and energetic analyses illustrated mechanisms by which flexible inhibitors achieved binding; we found ligand conformational adaptation near mutation sites and structural plasticity in targets through torsional flips of asymmetric functional groups to form alternative, compensatory packing interactions or hydrogen bonds. As no inhibitor bound to all variants, we designed small cocktails of inhibitors to do so and discovered that they often jointly covered the target set through mechanistic complementarity. Furthermore, using structural plasticity observed in experiments, and potentially in simulations, is suggested to be a viable means of designing adaptive inhibitors that are promiscuous binders. Proteins 2015; 83:351–372. © 2014 Wiley Periodicals, Inc.

An introduction to systems toxicology

Systems toxicology combines novel and historical experimental data to generate increasingly complex models of the biological response to chemical exposure.

Searching for synergistic bronchodilators and novel therapeutic regimens for chronic lung diseases from a traditional Chinese medicine, Qingfei Xiaoyan Wan

Classical Chinese pharmacopeias describe numerous excellent herbal formulations, and each prescription is an outstanding pool of effective compounds for drug discovery. Clarifying the bioactivity of the combined mechanisms of the ingredients in complex traditional Chinese medicine formulas is challenging. A classical formula known as Qingfei Xiaoyan Wan, used clinically as a treatment for prevalent chronic lung disease, was investigated in this work. A mutually enhanced bioactivity-guided ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS) characterization system was proposed, coupled with a dual-luciferase reporter assay for β₂AR-agonist cofactor screening. Arctiin, arctigenin, descurainoside and descurainolide B, four lignin compounds that showed synergistic bronchodilation effects with ephedrine, were revealed. The synergistic mechanism of arctigenin with the β₂ARagonist involved with the reduction of free Ca²⁺ was clarified by a dual-luciferase reporter assay for intracellular calcium and the Ca²⁺ indicator fluo-4/AM to monitor changes in the fluorescence. The relaxant and contractile responses of airway smooth muscle are regulated by crosstalk between the intracellular cAMP and calcium signaling pathways. Our data indicated the non-selective βAR agonist ephedrine as the principal bronchodilator of the formula, whereas the lignin ingredients served as adjuvant ingredients. A greater understanding of the mechanisms governing the control of these pathways, based on conventional wisdom, could lead to the identification of novel therapeutic targets or new agents for the treatment of asthma and COPD.

Intratumor heterogeneity alters most effective drugs in designed combinations

The substantial spatial and temporal heterogeneity observed in patient tumors poses considerable challenges for the design of effective drug combinations with predictable outcomes. Currently, the implications of tissue heterogeneity and sampling bias during diagnosis are unclear for selection and subsequent performance of potential combination therapies. Here, we apply a multiobjective computational optimization approach integrated with empirical information on efficacy and toxicity for individual drugs with respect to a spectrum of genetic perturbations, enabling derivation of optimal drug combinations for heterogeneous tumors comprising distributions of subpopulations possessing these perturbations. Analysis across probabilistic samplings from the spectrum of various possible distributions reveals that the most beneficial (considering both efficacy and toxicity) set of drugs changes as the complexity of genetic heterogeneity increases. Importantly, a significant likelihood arises that a drug selected as the most beneficial single agent with respect to the predominant subpopulation in fact does not reside within the most broadly useful drug combinations for heterogeneous tumors. The underlying explanation appears to be that heterogeneity essentially homogenizes the benefit of drug combinations, reducing the special advantage of a particular drug on a specific subpopulation. Thus, this study underscores the importance of considering heterogeneity in choosing drug combinations and offers a principled approach toward designing the most likely beneficial set, even if the subpopulation distribution is not precisely known.

Insulin in the brain: its pathophysiological implications for States related with central insulin resistance, type 2 diabetes and Alzheimer's disease

Although the brain has been considered an insulin-insensitive organ, recent reports on the location of insulin and its receptors in the brain have introduced new ways of considering this hormone responsible for several functions. The origin of insulin in the brain has been explained from peripheral or central sources, or both. Regardless of whether insulin is of peripheral origin or produced in the brain, this hormone may act through its own receptors present in the brain. The molecular events through which insulin functions in the brain are the same as those operating in the periphery. However, certain insulin actions are different in the central nervous system, such as hormone-induced glucose uptake due to a low insulin-sensitive GLUT-4 activity, and because of the predominant presence of GLUT-1 and GLUT-3. In addition, insulin in the brain contributes to the control of nutrient homeostasis, reproduction, cognition, and memory, as well as to neurotrophic, neuromodulatory, and neuroprotective effects. Alterations of these functional activities may contribute to the manifestation of several clinical entities, such as central insulin resistance, type 2 diabetes mellitus (T2DM), and Alzheimer's disease (AD). A close association between T2DM and AD has been reported, to the extent that AD is twice more frequent in diabetic patients, and some authors have proposed the name "type 3 diabetes" for this association. There are links between AD and T2DM through mitochondrial alterations and oxidative stress, altered energy and glucose metabolism, cholesterol modifications, dysfunctional protein O-GlcNAcylation, formation of amyloid plaques, altered Aβ metabolism, and tau hyperphosphorylation. Advances in the knowledge of preclinical AD and T2DM may be a major stimulus for the development of treatment for preventing the pathogenic events of these disorders, mainly those focused on reducing brain insulin resistance, which is seems to be a common ground for both pathological entities.

Decoding Alzheimer's disease from perturbed cerebral glucose metabolism: implications for diagnostic and therapeutic strategies

Alzheimer's disease (AD) is an age-related devastating neurodegenerative disorder, which severely impacts on the global economic development and healthcare system. Though AD has been studied for more than 100 years since 1906, the exact cause(s) and pathogenic mechanism(s) remain to be clarified. Also, the efficient disease-modifying treatment and ideal diagnostic method for AD are unavailable. Perturbed cerebral glucose metabolism, an invariant pathophysiological feature of AD, may be a critical contributor to the pathogenesis of this disease. In this review, we firstly discussed the features of cerebral glucose metabolism in physiological and pathological conditions. Then, we further reviewed the contribution of glucose transportation abnormality and intracellular glucose catabolism dysfunction in AD pathophysiology, and proposed a hypothesis that multiple pathogenic cascades induced by impaired cerebral glucose metabolism could result in neuronal degeneration and consequently cognitive deficits in AD patients. Among these pathogenic processes, altered functional status of thiamine metabolism and brain insulin resistance are highly emphasized and characterized as major pathogenic mechanisms. Finally, considering the fact that AD patients exhibit cerebral glucose hypometabolism possibly due to impairments of insulin signaling and altered thiamine metabolism, we also discuss some potential possibilities to uncover diagnostic biomarkers for AD from abnormal glucose metabolism and to develop drugs targeting at repairing insulin signaling impairment and correcting thiamine metabolism abnormality. We conclude that glucose metabolism abnormality plays a critical role in AD pathophysiological alterations through the induction of multiple pathogenic factors such as oxidative stress, mitochondrial dysfunction, and so forth. To clarify the causes, pathogeneses and consequences of cerebral hypometabolism in AD will help break the bottleneck of current AD study in finding ideal diagnostic biomarker and disease-modifying therapy.

Self-complementarity within proteins: bridging the gap between binding and folding

Complementarity, in terms of both shape and electrostatic potential, has been quantitatively estimated at protein-protein interfaces and used extensively to predict the specific geometry of association between interacting proteins. In this work, we attempted to place both binding and folding on a common conceptual platform based on complementarity. To that end, we estimated (for the first time to our knowledge) electrostatic complementarity (Em) for residues buried within proteins. Em measures the correlation of surface electrostatic potential at protein interiors. The results show fairly uniform and significant values for all amino acids. Interestingly, hydrophobic side chains also attain appreciable complementarity primarily due to the trajectory of the main chain. Previous work from our laboratory characterized the surface (or shape) complementarity (Sm) of interior residues, and both of these measures have now been combined to derive two scoring functions to identify the native fold amid a set of decoys. These scoring functions are somewhat similar to functions that discriminate among multiple solutions in a protein-protein docking exercise. The performances of both of these functions on state-of-the-art databases were comparable if not better than most currently available scoring functions. Thus, analogously to interfacial residues of protein chains associated (docked) with specific geometry, amino acids found in the native interior have to satisfy fairly stringent constraints in terms of both Sm and Em. The functions were also found to be useful for correctly identifying the same fold for two sequences with low sequence identity. Finally, inspired by the Ramachandran plot, we developed a plot of Sm versus Em (referred to as the complementarity plot) that identifies residues with suboptimal packing and electrostatics which appear to be correlated to coordinate errors.

Molecular networks in drug discovery

Despite the dramatic increase of global spending on drug discovery and development, the approval rate for new drugs is declining, due chiefly to toxicity and undesirable side effects. Simultaneously, the growth of available biomedical data in the postgenomic era has provided fresh insight into the nature of redundant and compensatory drug-target pathways. This stagnation in drug approval can be overcome by the novel concept of polypharmacology, which is built on the fundamental concept that drugs modulate multiple targets. Polypharmacology can be studied with molecular networks that integrate multidisciplinary concepts including cheminformatics, bioinformatics, and systems biology. In silico techniques such as structure- and ligand-based approaches can be employed to study molecular networks and reduce costs by predicting adverse drug reactions and toxicity in the early stage of drug development. By amalgamating strides in this informatics-driven era, designing polypharmacological drugs with molecular network technology exemplifies the next generation of therapeutics with less of-target properties and toxicity. In this review, we will first describe the challenges in drug discovery, and showcase successes using multitarget drugs toward diseases such as cancer and mood disorders. We will then focus on recent development of in silico polypharmacology predictions. Finally, our technologies in molecular network analysis will be presented.

Countering amyloid polymorphism and drug resistance with minimal drug cocktails

Several fatal, progressive neurodegenerative diseases, including various prion and prion-like disorders, are connected with the misfolding of specific proteins. These proteins misfold into toxic oligomeric species and a spectrum of distinct self-templating amyloid structures, termed strains. Hence, small molecules that prevent or reverse these protein-misfolding events might have therapeutic utility. Yet it is unclear whether a single small molecule can antagonize the complete repertoire of misfolded forms encompassing diverse amyloid polymorphs and soluble oligomers. We have begun to investigate this issue using the yeast prion protein Sup35 as an experimental paradigm. We have discovered that a polyphenol, (-)epigallocatechin-3-gallate (EGCG), effectively inhibited the formation of infectious amyloid forms (prions) of Sup35 and even remodeled preassembled prions. Surprisingly, EGCG selectively modulated specific prion strains and even selected for EGCG-resistant prion strains with novel structural and biological characteristics. Thus, treatment with a single small molecule antagonist of amyloidogenesis can select for novel, drug-resistant amyloid polymorphs. Importantly, combining EGCG with another small molecule, 4,5-bis-(4-methoxyanilino)phthalimide, synergistically antagonized and remodeled a wide array of Sup35 prion strains without producing any drug-resistant prions. We suggest that minimal drug cocktails, small collections of drugs that collectively antagonize all amyloid polymorphs, should be identified to besiege various neurodegenerative disorders.

Emergence and natural selection of drug-resistant prions

Drug resistance is a refractory barrier in the battle against many fatal diseases caused by rapidly evolving agents, including HIV, apicomplexans and specific cancers. Emerging evidence suggests that drug resistance might extend to lethal prion disorders and related neurodegenerative amyloidoses. Prions are self-replicating protein conformers, usually 'cross-beta' amyloid polymers, which are naturally transmitted between individuals and promote phenotypic change. Prion conformers are catalytic templates that specifically convert other copies of the same protein to the prion form. Once in motion, this chain reaction of conformational replication can deplete all non-prion copies of a protein. Typically, prions exist as ensembles of multiple structurally distinct, self-replicating forms or 'strains'. Each strain confers a distinct phenotype and replicates at different rates depending on the environment. As replicators, prions are units of selection. Thus, natural selection inescapably enriches or depletes various prion strains from populations depending on their conformational fitness (ability to self-replicate) in the prevailing environment. The most successful prions confer advantages to their host as with numerous yeast prions. Here, I review recent evidence that drug-like small molecules can antagonize some prion strains but simultaneously select for drug-resistant prions composed of mammalian PrP or the yeast prion protein, Sup35. For Sup35, the drug-resistant strain configures original intermolecular amyloid contacts that are not ordinarily detected. Importantly, a synergistic small-molecule cocktail counters prion diversity by eliminating multiple Sup35 prion strains. Collectively, these advances illuminate the plasticity of prionogenesis and suggest that synergistic combinatorial therapies might circumvent this pathological vicissitude.

SuperCYP: a comprehensive database on Cytochrome P450 enzymes including a tool for analysis of CYP-drug interactions

Much of the information on the Cytochrome P450 enzymes (CYPs) is spread across literature and the internet. Aggregating knowledge about CYPs into one database makes the search more efficient. Text mining on 57 CYPs and drugs led to a mass of papers, which were screened manually for facts about metabolism, SNPs and their effects on drug degradation. Information was put into a database, which enables the user not only to look up a particular CYP and all metabolized drugs, but also to check tolerability of drug-cocktails and to find alternative combinations, to use metabolic pathways more efficiently. The SuperCYP database contains 1170 drugs with more than 3800 interactions including references. Approximately 2000 SNPs and mutations are listed and ordered according to their effect on expression and/or activity. SuperCYP (http://bioinformatics.charite.de/supercyp) is a comprehensive resource focused on CYPs and drug metabolism. Homology-modeled structures of the CYPs can be downloaded in PDB format and related drugs are available as MOL-files. Within the resource, CYPs can be aligned with each other, drug-cocktails can be 'mixed', SNPs, protein point mutations, and their effects can be viewed and corresponding PubMed IDs are given. SuperCYP is meant to be a platform and a starting point for scientists and health professionals for furthering their research.

Synergistic drug combinations tend to improve therapeutically relevant selectivity

Drug combinations are a promising strategy to overcome the compensatory mechanisms and unwanted off-target effects that limit the utility of many potential drugs. However, enthusiasm for this approach is tempered by concerns that the therapeutic synergy of a combination will be accompanied by synergistic side effects. Using large scale simulations of bacterial metabolism and 94,110 multi-dose experiments relevant to diverse diseases, we provide evidence that synergistic drug combinations are generally more specific to particular cellular contexts than are single agent activities. We highlight six combinations whose selective synergy depends on multitarget drug activity. For one anti-inflammatory example, we show how such selectivity is achieved through differential expression of the drugs' targets in cell types associated with therapeutic, but not toxic, effects and validate its therapeutic relevance in a rat model of asthma. The context specificity of synergistic combinations creates many opportunities for therapeutically relevant selectivity and enables improved control of complex biological systems.

Network pharmacology: the next paradigm in drug discovery

The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.