Multicomponent phytotherapeutic approach gaining momentum: Is the “one drug to fit all” model breaking down?

Natural product based drugs constitute a substantial proportion of the pharmaceutical market particularly in the therapeutic areas of infectious diseases and oncology. The primary focus of any drug development program so far has been to design selective ligands (drugs) that act on single selective disease targets to obtain highly efficacious and safe drugs with minimal side effects. Although this approach has been successful for many diseases, yet there is a significant decline in the number of new drug candidates being introduced into clinical practice over the past few decades. This serious innovation deficit that the pharmaceutical industries are facing is due primarily to the post-marketing failures of blockbuster drugs. Many analysts believe that the current capital-intensive model-“the one drug to fit all” approach will be unsustainable in future and that a new “less investment, more drugs” model is necessary for further scientific growth. It is now well established that many diseases are multi-factorial in nature and that cellular pathways operate more like webs than highways. There are often multiple ways or alternate routes that may be switched on in response to the inhibition of a specific target. This gives rise to the resistant cells or resistant organisms under the specific pressure of a targeted agent, resulting in drug resistance and clinical failure of the drug. Drugs designed to act against individual molecular targets cannot usually combat multifactorial diseases like cancer, or diseases that affect multiple tissues or cell types such as diabetes and immunoinflammatory diseases. Combination drugs that affect multiple targets simultaneously are better at controlling complex disease systems and are less prone to drug resistance. This multicomponent therapy forms the basis of phytotherapy or phytomedicine where the holistic therapeutic effect arises as a result of complex positive (synergistic) or negative (antagonistic) interactions between different components of a cocktail. In this approach, multicomponent therapy is considered to be advantageous for multifactorial diseases, instead of a “magic bullet” the metaphor of a “herbal shotgun” might better explain the state of affairs. The different interactions between various components might involve the protection of an active substance from decomposition by enzymes, modification of transport across membranes of cells or organelles, evasion of multidrug resistance mechanisms among others.     

Study of drug function based on similarity of pathway fingerprint

Drugs sharing similar therapeutic function may not bind to the same group of targets. However, their targets may be involved in similar pathway profiles which are associated with certain pathological process. In this study, pathway fingerprint was introduced to indicate the profile of significant pathways being influenced by the targets of drugs. Then drug-drug network was further constructed based on significant similarity of pathway fingerprints. In this way, the functions of a drug may be hinted by the enriched therapeutic functions of its neighboring drugs. In the test of 911 FDA approved drugs with more than one known target, 471 drugs could be connected into networks. 760 significant associations of drug-therapeutic function were generated, among which around 60% of them were supported by scientific literatures or ATC codes of drug functional classification. Therefore, pathway fingerprints may be useful to further study on the potential function of known drugs, or the unknown function of new drugs.     

A Multidimensional Strategy to Detect Polypharmacological Targets in the Absence of Structural and Sequence Homology

Conventional drug design embraces the “one gene, one drug, one disease” philosophy. Polypharmacology, which focuses on multi-target drugs, has emerged as a new paradigm in drug discovery. The rational design of drugs that act via polypharmacological mechanisms can produce compounds that exhibit increased therapeutic potency and against which resistance is less likely to develop. Additionally, identifying multiple protein targets is also critical for side-effect prediction. One third of potential therapeutic compounds fail in clinical trials or are later removed from the market due to unacceptable side effects often caused by off-target binding. In the current work, we introduce a multidimensional strategy for the identification of secondary targets of known small-molecule inhibitors in the absence of global structural and sequence homology with the primary target protein. To demonstrate the utility of the strategy, we identify several targets of 4,5-dihydroxy-3-(1-naphthyldiazenyl)-2,7-naphthalenedisulfonic acid, a known micromolar inhibitor of Trypanosoma brucei RNA editing ligase 1. As it is capable of identifying potential secondary targets, the strategy described here may play a useful role in future efforts to reduce drug side effects and/or to increase polypharmacology.     

Regulation of therapeutic apoptosis: a potential target in controlling hypertensive organ damage

Cell growth and survival are potential therapeutic targets for the control of complications associated with hypertension. In most cardiovascular disorders, cardiac fibroblasts and large-vessel smooth muscle cells can replicate and thus contribute to the disease. We propose that cardiovascular hyperplasia may be reversed via therapeutic apoptosis induction with drugs that are safe and already used in the clinic. We first reported that, irrespective of the drug class, those drugs that are able to induce regression of cardiovascular hypertrophy are also able to reverse cardiovascular hyperplasia via apoptosis. Drugs active in this regard include inhibitors of the renin-angiotensin system, calcium channel blockers, and beta-blockers. Moreover, the effects of these drugs on cell survival is not merely secondary to blood pressure reduction. Therapeutic apoptosis in the cardiovascular system of the spontaneously hypertensive rat is characterized by a rapid and transient onset following initiation of antihypertensive treatment. Herein, the induction and termination of therapeutic apoptosis during drug treatment of hypertension will be briefly reviewed and supported by novel data suggesting that reversal of cardiovascular hyperplasia is associated with reduced cell growth and a resistance to further induction of therapeutic apoptosis, as shown in spontaneously hypertensive rats receiving an intermittent regime of nifedipine therapy. We propose that the presence of a cell subpopulation with defective cell cycle regulation may determine organ susceptibility to undergo therapeutic apoptosis.     

Development of a fission yeast-based high-throughput screen to identify chemical regulators of cAMP phosphodiesterases

Cyclic nucleotide phosphodiesterases (PDEs) comprise a superfamily of enzymes that serve as drug targets in many human diseases. There is a continuing need to identify high-specificity inhibitors that affect individual PDE families or even subtypes within a single family. The authors describe a fission yeast-based high-throughput screen to detect inhibitors of heterologously expressed adenosine 3',5'-cyclic monophosphate (cAMP) PDEs. The utility of this system is demonstrated by the construction and characterization of strains that express mammalian PDE2A, PDE4A, PDE4B, and PDE8A and respond appropriately to known PDE2A and PDE4 inhibitors. High-throughput screens of 2 bioactive compound libraries for PDE inhibitors using strains expressing PDE2A, PDE4A, PDE4B, and the yeast PDE Cgs2 identified known PDE inhibitors and members of compound classes associated with PDE inhibition. The authors verified that the furanocoumarin imperatorin is a PDE4 inhibitor based on its ability to produce a PDE4-specific elevation of cAMP levels. This platform can be used to identify PDE activators, as well as genes encoding PDE regulators, which could serve as targets for future drug screens.     

Serotonin (5HT), fluoxetine, imipramine and dopamine target distinct 5HT receptor signaling to modulate Caenorhabditis elegans egg-laying behavior

Drugs that target the serotonergic system are the most commonly prescribed therapeutic agents and are used for treatment of a wide range of behavioral and neurological disorders. However, the mechanism of the drug action remain a conjecture. Here, we dissect the genetic targets of serotonin (5HT), the selective 5HT reuptake inhibitor (SSRI) fluoxetine (Prozac), the tricyclic antidepressant imipramine, and dopamine. Using the well-established serotonergic response in C. elegans egg-laying behavior as a paradigm, we show that action of fluoxetine and imipramine at the 5HT reuptake transporter (SERT) and at 5HT receptors are separable mechanisms. Even mutants completely lacking 5HT or SERT can partially respond to fluoxetine and imipramine. Furthermore, distinct mechanisms for each drug can be recognized to mediate these responses. Deletion of SER-1, a 5HT1 receptor, abolishes the response to 5HT but has only a minor effect on the response to imipramine and no effect on the response to fluoxetine. In contrast, deletion of SER-4, a 5HT2 receptor, confers significant resistance to imipramine while leaving the responses to 5HT or fluoxetine intact. Further, fluoxetine can stimulate egg laying via the Gq protein EGL-30, independent of SER-1, SER-4, or 5HT. We also show that dopamine antagonizes the 5HT action via the 5HT-gated ion channel MOD-1 signaling, suggesting that this channel activity couples 5HT and dopamine signaling. These results suggest that the actions of these drugs at specific receptor subtypes could determine their therapeutic efficacy. SSRIs and tricyclic antidepressants may regulate 5HT outputs independently of synaptic levels of 5HT.     

Finding multiple target optimal intervention in disease‐related molecular network

Drugs against multiple targets may overcome the many limitations of single targets and achieve a more effective and safer control of the disease. Numerous high‐throughput experiments have been performed in this emerging field. However, systematic identification of multiple drug targets and their best intervention requires knowledge of the underlying disease network and calls for innovative computational methods that exploit the network structure and dynamics. Here, we develop a robust computational algorithm for finding multiple target optimal intervention (MTOI) solutions in a disease network. MTOI identifies potential drug targets and suggests optimal combinations of the target intervention that best restore the network to a normal state, which can be customer designed. We applied MTOI to an inflammation‐related network. The well‐known side effects of the traditional non‐steriodal anti‐inflammatory drugs and the recently recalled Vioxx were correctly accounted for in our network model. A number of promising MTOI solutions were found to be both effective and safer.     

Liposomal-delivery of phosphodiesterase 5 inhibitors augments UT-15C-stimulated ATP release from human erythrocytes

The use of liposomes to affect targeted delivery of pharmaceutical agents to specific sites may result in the reduction of side effects and an increase in drug efficacy. Since liposomes are delivered intravascularly, erythrocytes, which constitute almost half of the volume of blood, are ideal targets for liposomal drug delivery.In vivo, erythrocytes serve not only in the role of oxygen transport but also as participants in the regulation of vascular diameter through the regulated release of the potent vasodilator, adenosine triphosphate (ATP). Unfortunately, erythrocytes of humans with pulmonary arterial hypertension (PAH) do not release ATP in response to the physiological stimulus of exposure to increases in mechanical deformation as would occur when these cells traverse the pulmonary circulation. This defect in erythrocyte physiology has been suggested to contribute to pulmonary hypertension in these individuals.In contrast to deformation, both healthy human and PAH erythrocytes do release ATP in response to incubation with prostacyclin analogs via a well-characterized signaling pathway. Importantly, inhibitors of phosphodiesterase 5 (PDE5) have been shown to significantly increase prostacyclin analog-induced ATP release from human erythrocytes.Here we investigate the hypothesis that targeted delivery of PDE5 inhibitors to human erythrocytes, using a liposomal delivery system, potentiates prostacyclin analog- induced ATP release. The findings are consistent with the hypothesis that directed delivery of this class of drugs to erythrocytes could be a new and important method to augment prostacyclin analog-induced ATP release from these cells. Such an approach could significantly limit side effects of both classes of drugs without compromising their therapeutic effectiveness in diseases such as PAH.     

Quantitative analysis on the characteristics of targets with FDA approved drugs

Accumulated knowledge of genomic information, systems biology, and disease mechanisms provide an unprecedented opportunity to elucidate the genetic basis of diseases, and to discover new and novel therapeutic targets from the wealth of genomic data. With hundreds to a few thousand potential targets available in the human genome alone, target selection and validation has become a critical component of drug discovery process. The explorations on quantitative characteristics of the currently explored targets (those without any marketed drug) and successful targets (targeted by at least one marketed drug) could help discern simple rules for selecting a putative successful target. Here we use integrative in silico (computational) approaches to quantitatively analyze the characteristics of 133 targets with FDA approved drugs and 3120 human disease genes (therapeutic targets) not targeted by FDA approved drugs. This is the first attempt to comparatively analyze targets with FDA approved drugs and targets with no FDA approved drug or no drugs available for them. Our results show that proteins with 5 or fewer number of homologs outside their own family, proteins with single-exon gene architecture and proteins interacting with more than 3 partners are more likely to be targetable. These quantitative characteristics could serve as criteria to search for promising targetable disease genes.     

The relationship between rational drug design and drug side effects

Previous analysis of systems pharmacology has revealed a tendency of rational drug design in the pharmaceutical industry. The targets of new drugs tend to be close with the corresponding disease genes in the biological networks. However, it remains unclear whether the rational drug design introduces disadvantages, i.e. side effects. Therefore, it is important to dissect the relationship between rational drug design and drug side effects. Based on a recently released drug side effect database, SIDER, here we analyzed the relationship between drug side effects and the rational drug design. We revealed that the incidence drug side effect is significantly associated with the network distance of drug targets and diseases genes. Drugs with the distances of three or four have the smallest incidence of side effects, whereas drugs with the distances of more than four or smaller than three show significantly greater incidence of side effects. Furthermore, protein drugs and small molecule drugs show significant differences. Drugs hitting membrane targets and drugs hitting cytoplasm targets also show differences. Failure drugs because of severe side effects show smaller network distances than approved drugs. These results suggest that researchers should be prudent on rationalizing the drug design. Too small distances between drug targets and diseases genes may not always be advantageous for rational design for drug discovery.     

Genomes2Drugs: Identifies Target Proteins and Lead Drugs from Proteome Data

Background

Genome sequencing and bioinformatics have provided the full hypothetical proteome of many pathogenic organisms. Advances in microarray and mass spectrometry have also yielded large output datasets of possible target proteins/genes. However, the challenge remains to identify new targets for drug discovery from this wealth of information. Further analysis includes bioinformatics and/or molecular biology tools to validate the findings. This is time consuming and expensive, and could fail to yield novel drugs if protein purification and crystallography is impossible. To pre-empt this, a researcher may want to rapidly filter the output datasets for proteins that show good homology to proteins that have already been structurally characterised or proteins that are already targets for known drugs. Critically, those researchers developing novel antibiotics need to select out the proteins that show close homology to any human proteins, as future inhibitors are likely to cross-react with the host protein, causing off-target toxicity effects later in clinical trials.

Methodology/Principal Findings

To solve many of these issues, we have developed a free online resource called Genomes2Drugs which ranks sequences to identify proteins that are (i) homologous to previously crystallized proteins or (ii) targets of known drugs, but are (iii) not homologous to human proteins. When tested using the Plasmodium falciparum malarial genome the program correctly enriched the ranked list of proteins with known drug target proteins.

Conclusions/Significance

Genomes2Drugs rapidly identifies proteins that are likely to succeed in drug discovery pipelines. This free online resource helps in the identification of potential drug targets. Importantly, the program further highlights proteins that are likely to be inhibited by FDA-approved drugs. These drugs can then be rapidly moved into Phase IV clinical studies under ‘change-of-application’ patents.     

基于生物信息学方法发现潜在药物靶标

药物靶点通常是在代谢或信号通路中与特定疾病或病理状态有关的关键分子.通过绑定到特定活动区域抑制这个关键分子进行药物设计.确定特定疾病有关的靶标分子是现代新药开发的基础.在药物靶标发现的过程中,生物信息学方法发挥了不可替代的重要的作用,尤其适用于大规模多组学数据的分析.目前,已涌现了许多与疾病相关的数据库资源,基于生物网络特征、多基因芯片、蛋白质组、代谢组数据等建立了多种生物信息学方法发现潜在的药物靶标,并预测靶标可药性和药物副作用.     

Developing combinatorial multi-component therapies (CMCT) of drugs that are more specific and have fewer side effects than traditional one drug therapies

Drugs designed for a specific target are always found to have multiple effects. Rather than hope that one bullet can be designed to hit only one target, nonlinear interactions across genomic and proteomic networks could be used to design Combinatorial Multi-Component Therapies (CMCT) that are more targeted with fewer side effects. We show here how computational approaches can be used to predict which combinations of drugs would produce the best effects. Using a nonlinear model of how the output effect depends on multiple input drugs, we show that an artificial neural network can accurately predict the effect of all 2¹⁵ = 32,768 combinations of drug inputs using only the limited data of the output effect of the drugs presented one-at-a-time and pairs-at-a-time.     

Effect of Ca2+-antagonists on virally-induced cell-permeability changes

Sendai virus-mediated permeability changes in cells are affected by extracellular Ca2+ or Mn2+ as follows: the lag period to onset of permeability changes is lengthened and the subsequent extent of leakage is reduced. Drugs that block Ca2+ action in excitable cells, such as verapamil and prenylamine, and drugs that inhibit the action of calmodulin, such as trifluoperazine and R24571, have an effect opposite to that of Ca2+: lag is shortened and extent of leakage is increased. The concentration at which either type of drug shows 50% of maximal effect is similar to the concentration at which 50% of binding by drug to calmodulin is achieved. It is concluded that calmodulin may be involved in protecting cells against virally-mediated membrane damage; alternatively the action of calmodulin-binding drugs may not be as specific as currently thought.     

Phosphodiesterase 4 inhibitors and drugs of abuse: current knowledge and therapeutic opportunities

Background

Long-term exposure to drugs of abuse causes an upregulation of the cAMP-signaling pathway in the nucleus accumbens and other forebrain regions, this common neuroadaptation is thought to underlie aspects of drug tolerance and dependence. Phosphodiesterase 4 (PDE4) is an enzyme that the selective hydrolyzes intracellular cAMP. It is expressed in several brain regions that regulate the reinforcing effects of drugs of abuse.

Objective

Here, we review the current knowledge about central nervous system (CNS) distribution of PDE4 isoforms and the effects of systemic and brain-region specific inhibition of PDE4 on behavioral models of drug addiction.

Methods

A systematic literature search was performed using the Pubmed.

Results

Using behavioral sensitization, conditioned place preference and drug self-administration as behavioral models, a large number of studies have shown that local or systemic administration of PDE4 inhibitors reduce drug intake and/or drug seeking for psychostimulants, alcohol, and opioids in rats or mice.

Conclusions

Preclinical studies suggest that PDE4 could be a therapeutic target for several classes of substance use disorder. We conclude by identifying opportunities for the development of subtype-selective PDE4 inhibitors that may reduce addiction liability and minimize the side effects that limit the clinical potential of non-selective PDE4 inhibitors. Several PDE4 inhibitors have been clinically approved for other diseases. There is a promising possibility to repurpose these PDE4 inhibitors for the treatment of drug addiction as they are safe and well-tolerated in patients.

     

磷酸二酯酶的心血管功能调节作用

磷酸二酯酶(phosphodiesterase,PDE)是细胞内第二信使cAMP和cGMP降解的关键酶,作为药物研发的靶点受到广泛关注。近年研究发现,PDE在心肌细胞中能与β-肾上腺素受体及一些与兴奋收缩相关的蛋白形成复合物而使细胞内信号传递区室化分布,该现象可能为PDE抑制剂治疗慢性心力衰竭提供新的启示。血管平滑肌功能调节主要为血管张力和表型的调控,PDE5抑制剂舒张血管的作用已成功应用到勃起障碍的治疗。PDE4和PDE1C等在增殖的平滑肌细胞中表达量增高,单独抑制PDE的某一亚型将为治疗与平滑肌增殖有关的疾病(如肺动脉高压、血管成行术后再狭窄)提供新的途径。本文将重点阐述近年来PDE在心血管系统功能调节研究中的主要进展,以及PDE抑制剂在心血管系统疾病治疗中的应用。     

A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism

Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the “effect space” comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism.     

Analysis of non-structural proteins,NSPs of SARS-CoV-2 as targets for computational drug designing

BackgroundThe ORF1ab of Severe Acute Respiratory Syndrome, SARS Corona Virus, SARS-CoV-2 genome is processed into 15 non-structural proteins, NSPs by proteases and each NSP has a specific role in the life cycle and pathogenicity of the virus. This research analyzes possible drugs for these proteins as targets in computational drug designing using already available experimental drugs from the drug bank database.MethodsOut of 471 proteins and 8820 drugs download from Protein Data Bank, PDB and Drug Bank database respectively, 16 proteins similar to NSP 1–15 and 31 drugs as per the “Rule of three” were selected for docking. Out of 88 docking results using PyRx, 18 proteins/chains with three promising drugs, DB01977, DB07132 and DB07535 were analyzed using PyMOL for final results.ResultsNSPs 3, 5, 11, 14 and 15 were identified as targets for the drugs, DB01977, BD07132 and DB07535. Drugs, DB01977 and DB07535 bind in the same binding pockets of NSP 5 and NSP 15. Drug, DB07132 binds with more number of residues when compared with the other two drugs and this indicates that the strength of protein-drug association is more by this drug with the NSPs than other drugs. Binding pockets of NSPs for these three drugs are very close with many sharing residues in common suggesting of similarity of pharmacophore of these drugs with the target binding pockets.ConclusionThe binding pockets of NSPs are well matched with the pharmacophore of drugs and with polar surface of drugs less than or equal to 100 A², drugs, DB01977, DB07132 and DB07535 bind individually and effectively with NSPs 3, 5, 11, 14 and 15 of ORF1ab of SARS-CoV-2 genome to bring changes in the activity of SARS-CoV-2 which may be useful for biological and clinical considerations.     

Elimination of permeability mutants from selections for drug resistance in mammalian cells

Chinese hamster ovary (CHO) cells exhibit increased sensitivity to a wide variety of microtubule inhibitory drugs when verapamil is present in the growth medium. The extent of this increased sensitivity is drug specific: some drugs such as taxol and vinblastine respond greatly to the presence of verapamil, whereas other drugs such as griseofulvin respond very poorly. For the majority of drugs examined, however, a 2- to 10-fold increase in drug sensitivity is observed in the presence of verapamil at 5 micrograms/ml. The effects of verapamil are even more dramatic when drug-resistant mutant cells with a presumed alteration in membrane permeability are examined. In the presence of appropriate levels of verapamil, these mutants demonstrate a level of drug sensitivity comparable to that of the wild-type parental cells. Drug-resistant cells from similar selections but with well-defined alterations in alpha- or beta-tubulin and no evidence of alterations in membrane permeability, however, continue to exhibit increased resistance to the selecting drug even in the presence of verapamil. These studies support the conclusion that verapamil affects the membrane permeability to or transport of a wide variety of hydrophobic drugs. In addition, we have used this information to devise selections that virtually eliminate the isolation of drug-resistant permeability mutants. This methodology should be generally applicable to genetic studies of drug action that are complicated by the isolation of large numbers of mutants with permeability alterations.