Modeling drug action in the mouse with knockouts and RNA interference

Personalization of medical treatments in oncology: time for rethinking the disease concept to improve individual outcomes

The agenda of pharmacology discovery in the field of personalized oncology was dictated by the search of molecular targets assumed to deterministically drive tumor development. In this perspective, genes play a fundamental "causal" role while cells simply act as causal proxies, i.e., an intermediate between the molecular input and the organismal output. However, the ceaseless genomic change occurring across time within the same primary and metastatic tumor has broken the hope of a personalized treatment based only upon genomic fingerprint. Indeed, current models are unable in capturing the unfathomable complexity behind the outbreak of a disease, as they discard the contribution of non-genetic factors, environment constraints, and the interplay among different tiers of organization. Herein, we posit that a comprehensive personalized model should view at the disease as a "historical" process, in which different spatially and timely distributed factors interact with each other across multiple levels of organization, which collectively interact with a dynamic gene-expression pattern. Given that a disease is a dynamic, non-linear process - and not a static-stable condition - treatments should be tailored according to the "timing-frame" of each condition. This approach can help in detecting those critical transitions through which the system can access different attractors leading ultimately to diverse outcomes - from a pre-disease state to an overt illness or, alternatively, to recovery. Identification of such tipping points can substantiate the predictive and the preventive ambition of the Predictive, Preventive and Personalized Medicine (PPPM/3PM). However, an unusual effort is required to conjugate multi-omics approaches, data collection, and network analysis reconstruction (eventually involving innovative Artificial Intelligent tools) to recognize the critical phases and the relevant targets, which could help in patient stratification and therapy personalization.

Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models

Research on new cancer drugs is performed either through gene knockout studies or phenotypic screening of drugs in cancer cell-lines. Both of these approaches are costly and time-consuming. Computational framework, e.g., genome-scale metabolic models (GSMMs), could be a good alternative to find potential drug targets. The present study aims to investigate the applicability of gene knockout strategies to be used as the finding of drug targets using GSMMs. We performed single-gene knockout studies on existing GSMMs of the NCI-60 cell-lines obtained from 9 tissue types. The metabolic genes responsible for the growth of cancerous cells were identified and then ranked based on their cellular growth reduction. The possible growth reduction mechanisms, which matches with the gene knockout results, were described. Gene ranking was used to identify potential drug targets, which reduce the growth rate of cancer cells but not of the normal cells. The gene ranking results were also compared with existing shRNA screening data. The rank-correlation results for most of the cell-lines were not satisfactory for a single-gene knockout, but it played a significant role in deciding the activity of drug against cell proliferation, whereas multiple gene knockout analysis gave better correlation results. We validated our theoretical results experimentally and showed that the drugs mitotane and myxothiazol can inhibit the growth of at least four cell-lines of NCI-60 database.

A whole genome bioinformatic approach to determine potential latent phase specific targets in Mycobacterium tuberculosis

Current Tuberculosis treatment is long and expensive, faces the increasing burden of MDR/XDR strains and lack of effective treatment against latent form, resulting in an urgent need of new anti-TB drugs. Key to TB biology is its capacity to fight the host's RNOS mediated attack. RNOS are known to display a concentration dependent mycobactericidal activity, which leads to the following hypothesis "if we know which proteins are targeted by RNOS and kill TB, we we might be able to inhibit them with drugs resulting in a synergistic bactericidal effect". Based on this idea, we performed an Mtb metabolic network whole proteome analysis of potential RNOS sensitive and relevant targets which includes target druggability and essentiality criteria. Our results, available at http://tuberq.proteinq.com.ar yield new potential TB targets, like I3PS, while also providing and updated view of previous proposals becoming an important tool for researchers looking for new ways of killing TB.

Predicting drug-target interactions using probabilistic matrix factorization

Quantitative analysis of known drug-target interactions emerged in recent years as a useful approach for drug repurposing and assessing side effects. In the present study, we present a method that uses probabilistic matrix factorization (PMF) for this purpose, which is particularly useful for analyzing large interaction networks. DrugBank drugs clustered based on PMF latent variables show phenotypic similarity even in the absence of 3D shape similarity. Benchmarking computations show that the method outperforms those recently introduced provided that the input data set of known interactions is sufficiently large--which is the case for enzymes and ion channels, but not for G-protein coupled receptors (GPCRs) and nuclear receptors. Runs performed on DrugBank after hiding 70% of known interactions show that, on average, 88 of the top 100 predictions hit the hidden interactions. De novo predictions permit us to identify new potential interactions. Drug-target pairs implicated in neurobiological disorders are overrepresented among de novo predictions.

Network pharmacology: a new approach for chinese herbal medicine research

The dominant paradigm of "one gene, one target, one disease" has influenced many aspects of drug discovery strategy. However, in recent years, it has been appreciated that many effective drugs act on multiple targets rather than a single one. As an integrated multidisciplinary concept, network pharmacology, which is based on system biology and polypharmacology, affords a novel network mode of "multiple targets, multiple effects, complex diseases" and replaces the "magic bullets" by "magic shotguns." Chinese herbal medicine (CHM) has been recognized as one of the most important strategies in complementary and alternative medicine. Though CHM has been practiced for a very long time, its effectiveness and beneficial contribution to public health has not been fully recognized. Also, the knowledge on the mechanisms of CHM formulas is scarce. In the present review, the concept and significance of network pharmacology is briefly introduced. The application and potential role of network pharmacology in the CHM fields is also discussed, such as data collection, target prediction, network visualization, multicomponent interaction, and network toxicology. Furthermore, the developing tendency of network pharmacology is also summarized, and its role in CHM research is discussed.

Phenotypic and In Vivo Screening: Lead Discovery and Drug Repurposing

The changes in screening philosophy over a 40 year period from in vivo phenotypic screening to a reductionist mechanism-based in vitro search for a single selective compound against a single target are described. Examples are given of the shortcomings of the reductionist paradigm and the advantages of the phenotypic and multi-target screening approaches towards drug discovery and repurposing. Non-mechanism biased phenotypic screening offers the advantages of enhanced target opportunity space and is a good match for screening of ligands covering narrow chemistry space, e.g. natural products. Retrospective analysis suggests that phenotypic screening is better than mechanistic screening in finding first in class compounds, particularly for the more complex disease targets.

Evaluation of plasma membrane calcium/calmodulin-dependent ATPase isoform 4 as a potential target for fertility control

The array of contraceptives currently available is clearly inadequate and does not meet consumer demands since it is estimated that up to a quarter of all pregnancies worldwide are unintended. There is, therefore, an overwhelming global need to develop new effective, safe, ideally non-hormonal contraceptives for both male and female use. The contraceptive field, unlike other areas such as cancer, has a dearth of new targets. We have addressed this issue and propose that isoform 4 of the plasma membrane calcium ATPase is a potentially exciting novel target for fertility control. The plasma membrane calcium ATPase is a ubiquitously expressed calcium pump whose primary function in the majority of cells is to extrude calcium to the extracellular milieu. Two isoforms of this gene family, PMCA1 and PMCA4, are expressed in spermatozoa, with PMCA4 being the predominant isoform. Although this gene is ubiquitously expressed, its function is highly tissue-specific. Genetic deletion of PMCA4, in PMCA4 knockout mice, led to 100% infertility specifically in the male mutant mice due to a selective defect in sperm motility. It is important to note that the gene deletion did not affect normal mating characteristics in these mice. This phenotype was mimicked in wild-type sperm treated with the non-specific PMCA inhibitor 5-(and 6-) carboxyeosin diacetate succinimidyl ester; a proof-of-principle that inhibition of PMCA4 has potential importance in the control of fertility. This review outlines the potential for PMCA4 to be a novel target for fertility control by acting to inhibit sperm motility. It will outline the characteristics that make this target drugable and will describe methodologies to identify and validate novel inhibitors of this target.

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.

New biological therapies from the human genome

Over the past 20 years, the biotechnology industry has been extraordinarily successful in bringing a wide variety of new products to the market, including recombinant versions of natural proteins such as growth hormone, insulin and the gonadotropins. The availability of the human genome sequence has given us the chance to identify the entire catalogue of human secreted proteins, often called the secretome. One of the challenges of biotechnology research has been to identify the biological activities of these proteins and to identify if any of them could have a therapeutic or pharmacologic use. The paradigm has effectively been reversed, in that it used to be easy to know the biological activity, but difficult to clone, whereas now the contrary is true. Five years on, it is clear that finding new biological activities is a very difficult process. Much of the ground gained in this period has either been through the development of antibodies as therapies or by the use of protein engineering to produce better versions of the proteins that are already being produced.

Human monogenic disorders — a source of novel drug targets

The decrease in new drug applications and approvals over the past several years results from an underlying crisis in drug target identification and validation. Model organisms are being used to address this problem, in combination with novel approaches such as the International HapMap Project. What has been underappreciated is that discovery of new drug targets can also be revived by traditional Mendelian genetics. A large fraction of the human gene repertoire remains phenotypically uncharacterized, and is likely to encode many unanticipated and novel phenotypes that will be of interest to pharmaceutical and biotechnological drug developers.

Navigating chemical space for biology and medicine

Despite over a century of applying organic synthesis to the search for drugs, we are still far from even a cursory examination of the vast number of possible small molecules that could be created. Indeed, a thorough examination of all 'chemical space' is practically impossible. Given this, what are the best strategies for identifying small molecules that modulate biological targets? And how might such strategies differ, depending on whether the primary goal is to understand biological systems or to develop potential drugs?