Molecular Considerations In The Design Of Novel Alpha/Beta Hydrolase Inhibitors

Alpha/beta hydrolases (ABHs) are a superfamily of hydrolytic enzymes that process a wide variety of substrates. A subfamily of ABHs called carboxylesterases (CEs) are important enzymes that catalyze biological detoxification, hydrolysis of certain pesticides, and metabolism of many esterified drugs. The chemotherapy drug irinotecan used for treatment of colorectal cancer is metabolized to SN-38, the active drug metabolite, by two CE isozymes CES1 (localized in the liver) and CES2 (localized in the small intestines). CES2\u27s ability to activate irinotecan at a faster rate than CES1 creates a localization of activated SN-38 in the gut epithelium, resulting in the dose limiting side effect of delayed diarrhea. Development of inhibitors for the CE subfamily of ABHs could assist in ameliorating the toxic side effects associated with some esterified prodrugs such as irinotecan, and enhance the distribution of prodrugs in vivo. Hence, our research targets CES2 for inhibitor design with the goal of amelioration of intestinal cytotoxicity associated with irinotecan chemotherapy. In this work we (i) utilized QSAR technology to design and optimize novel sulfonamide CES2 inhibitors; (ii) combined QSAR with in silico design to generate new CE inhibitor scaffolds that maintained the potency of previous CE inhibitor generations, yet had improved water solubility; and ( iii) investigated the contribution of the loop 7 in CEs to sensitizing the enzyme to inhibition by sulfonamides through docking analysis. Our QSAR model, developed using 57 sulfonamide analogs, identified several features of this class of CE inhibitor that confer their potency. Using a QSAR model, constructed using 4 classes of CE inhibitors (benzils, benzoins, isatins, and sulfonamides), as a pocket site to perform in silico design we generated several new scaffolds predicted to have good solubility and potency. This work suggests that the inner loop 7 on CE plays a role in inhibitor selectivity, and interactions with this loop should be considered in the development of selective CE inhibitors. The contributions from this work will be applicable to the design of novel ABH inhibitors, help to increase the likelihood of these drugs entering in clinical use, and ameliorate the dose-limiting side effect associated with irinotecan

Evolutionary Computation and QSAR Research

[Abstract] The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.Instituto de Salud Carlos III, PIO52048Instituto de Salud Carlos III, RD07/0067/0005Ministerio de Industria, Comercio y Turismo; TSI-020110-2009-53)Galicia. Consellería de Economía e Industria; 10SIN105004P

Structural Determination of Three Different Series of Compounds as Hsp90 Inhibitors Using 3D-QSAR Modeling, Molecular Docking and Molecular Dynamics Methods

Hsp90 is involved in correcting, folding, maturation and activation of a diverse array of client proteins; it has also been implicated in the treatment of cancer in recent years. In this work, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), molecular docking and molecular dynamics were performed on three different series of Hsp90 inhibitors to build 3D-QSAR models, which were based on the ligand-based or receptor-based methods. The optimum 3D-QSAR models exhibited reasonable statistical characteristics with averaging internal q2 > 0.60 and external r2pred > 0.66 for Benzamide tetrahydro-4H-carbazol-4-one analogs (BT), AT13387 derivatives (AT) and Dihydroxylphenyl amides (DA). The results revealed that steric effects contributed the most to the BT model, whereas H-bonding was more important to AT, and electrostatic, hydrophobic, H-bond donor almost contributed equally to the DA model. The docking analysis showed that Asp93, Tyr139 and Thr184 in Hsp90 are important for the three series of inhibitors. Molecular dynamics simulation (MD) further indicated that the conformation derived from docking is basically consistent with the average structure extracted from MD simulation. These results not only lead to a better understanding of interactions between these inhibitors and Hsp90 receptor but also provide useful information for the design of new inhibitors with a specific activity

Nature is the best source of anti-inflammatory drugs: indexing natural products for their anti-inflammatory bioactivity

Objectives The aim was to index natural products for less expensive preventive or curative anti-inflammatory therapeutic drugs. Materials A set of 441 anti-inflammatory drugs representing the active domain and 2892 natural products representing the inactive domain was used to construct a predictive model for bioactivity-indexing purposes. Method The model for indexing the natural products for potential anti-inflammatory activity was constructed using the iterative stochastic elimination algorithm (ISE). ISE is capable of differentiating between active and inactive antiinflammatory molecules. Results By applying the prediction model to a mix set of (active/inactive) substances, we managed to capture 38% of the anti-inflammatory drugs in the top 1% of the screened set of chemicals, yielding enrichment factor of 38. Ten natural products that scored highly as potential antiinflammatory drug candidates are disclosed. Searching the PubMed revealed that only three molecules (Moupinamide, Capsaicin, and Hypaphorine) out of the ten were tested and reported as anti-inflammatory. The other seven phytochemicals await evaluation for their anti-inflammatory activity in wet lab. Conclusion The proposed anti-inflammatory model can be utilized for the virtual screening of large chemical databases and for indexing natural products for potential antiinflammatory activity.This work was partially supported by the Al- Qasemi Research Foundation (Grant no. 954000) and the Ministry of Science, Space and Technology, Israel. We declare that the funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript

COMPUTATIONAL DESIGN OF 3-PHOSPHOINOSITIDE DEPENDENT KINASE-1 INHIBITORS AS POTENTIAL ANTI-CANCER AGENTS

Computational drug design methods have great potential in drug discovery particularly in lead identification and lead optimization. 3-Phosphoinositide dependent kinase-1 (PDK1) is a protein kinase and a well validated anti-cancer target. Inhibitors of PDK1 have the potential to be developed as anti-cancer drugs. In this work, we have applied various novel computational drug design strategies to design and identify new PDK1 inhibitors with potential anti-cancer activity. We have pursued novel structure-based drug design strategies and identified a new binding mode for celecoxib and its derivatives binding with PDK1. This new binding mode provides a valuable basis for rational design of potent PDK1 inhibitors. In order to understand the structure-activity relationship of indolinone-based PDK1 inhibitors, we have carried out a combined molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling study. The predictive ability of the developed 3D-QSAR models were validated using an external test set of compounds. An efficient strategy of the hierarchical virtual screening with increasing complexity was pursued to identify new hits against PDK1. Our approach uses a combination of ligand-based and structure-based virtual screening including shape-based filtering, rigid docking, and flexible docking. In addition, a more sophisticated molecular dynamics/molecular mechanics- Poisson-Boltzmann surface area (MD/MM-PBSA) analysis was used as the final filter in the virtual screening. Our screening strategy has led to the identification of a new PDK1 inhibitor. The anticancer activities of this compound have been confirmed by the anticancer activity assays of national cancer institute-developmental therapeutics program (NCI-DTP) using 60 cancer cell lines. The PDK1-inhibitor binding mode determined in this study may be valuable in future de novo drug design. The virtual screening approach tested and used in this study could also be applied to lead identification in other drug discovery efforts

Cholinesterase Research

This collection of 10 papers includes original as well as review articles focused on the cholinesterase structural aspects, drug design and development of novel cholinesterase ligands, but also contains papers focused on the natural compounds and their effect on the cholinergic system and unexplored effects of donepezil

Assessment of In-House Natural Product and Synthetic Compound Libraries Based on In vitro Inhibition of Cholinesterases

The first line medication for mild to moderate Alzheimer s disease (AD) is based on cholinesterase inhibitors which prolong the effect of the neurotransmitter acetylcholine in cholinergic nerve synapses which relieves the symptoms of the disease. Implications of cholinesterases involvement in disease modifying processes has increased interest in this research area. The drug discovery and development process is a long and expensive process that takes on average 13.5 years and costs approximately 0.9 billion US dollars. Drug attritions in the clinical phases are common due to several reasons, e.g., poor bioavailability of compounds leading to low efficacy or toxic effects. Thus, improvements in the early drug discovery process are needed to create highly potent non-toxic compounds with predicted drug-like properties. Nature has been a good source for the discovery of new medicines accounting for around half of the new drugs approved to market during the last three decades. These compounds are direct isolates from the nature, their synthetic derivatives or natural mimics. Synthetic chemistry is an alternative way to produce compounds for drug discovery purposes. Both sources have pros and cons. The screening of new bioactive compounds in vitro is based on assaying compound libraries against targets. Assay set-up has to be adapted and validated for each screen to produce high quality data. Depending on the size of the library, miniaturization and automation are often requirements to reduce solvent and compound amounts and fasten the process. In this contribution, natural extract, natural pure compound and synthetic compound libraries were assessed as sources for new bioactive compounds. The libraries were screened primarily for acetylcholinesterase inhibitory effect and secondarily for butyrylcholinesterase inhibitory effect. To be able to screen the libraries, two assays were evaluated as screening tools and adapted to be compatible with special features of each library. The assays were validated to create high quality data. Cholinesterase inhibitors with various potencies and selectivity were found in natural product and synthetic compound libraries which indicates that the two sources complement each other. It is acknowledged that natural compounds differ structurally from compounds in synthetic compound libraries which further support the view of complementation especially if a high diversity of structures is the criterion for selection of compounds in a library.Koliiniesteraasien estäjiä käytetään lievän tai keskivaikean Alzheimerin taudin ensisijaisena lääkehoitona. Koliiniesteraasien estäjät pidentävät asetyylikoliini-hermovälittäjäaineen vaikutusta hermosynapseissa, mikä johtaa Alzheimerin taudin oireiden lievenemiseen. Viitteet koliiniesteraasien estäjien mahdollisista vaikutuksista myös taudin etenemiseen ovat lisänneet kiinnostusta tätä tutkimusalaa kohtaan. Lääkkeen keksimis- ja kehittämisprosessi on aikaa vievää ja kallista. Keskimäärin aikaa kuluu 13,5 vuotta ja koko prosessi maksaa noin 0,9 miljardia Yhdysvaltain dollaria. Lääkekandidaatin hylkääminen kesken kliinisten kokeiden on yleistä. Syitä hylkäämiseen on useita esim. huono biologinen hyötyosuus, mikä voi johtaa muun muassa heikkoon tehoon. Lääkkeenkeksimisprosessin alkuvaiheiden kehittäminen parantaa mahdollisuuksia löytää tehokkaita ja turvallisia lääkkeenkaltaisia yhdisteitä. Lääkkeenkaltaisten yhdisteiden etuna on se, että niiden käyttäytyminen on ennustettavissa siirryttäessä lääkkeenkeksimisvaiheesta eteenpäin lääkekehitykseen. Luonto on ollut hyvä lähde uusien lääkeaineiden etsimisessä. Noin puolet viimeisten kolmen vuosikymmenen aikana markkinoille hyväksytyistä lääkeaineista on peräisin luonnosta. Nämä yhdisteet on joko eristetty suoraan luonnosta, ne ovat luonnonaineiden synteettisiä johdoksia tai rakenne on muuten matkittu luonnosta peräisin olevista yhdisteistä. Toinen tapa tuottaa uusia molekyylejä lääkkeenkeksimisprosessia varten on synteettisen kemian hyödyntäminen. Näissä molemmissa lähestymistavoissa on omat hyvät ja huonot puolensa. Uusien lääkeainekandidaattien seulominen in vitro perustuu siihen, että kohde, joka tässä työssä oli proteiini, altistetaan kirjastoissa oleville näytteille ja mitataan muuttuuko kohteen toiminta. Koejärjestely täytyy validoida seulontaa varten, jotta seulonnan tulokset ovat laadukkaita ja luotettavia. Kun seulotaan tuhansia näytteitä, on kokeen miniatyrisoiminen ja automatisoiminen välttämätöntä liuosten ja tutkittavien yhdisteiden kulutuksen vähentämiseksi sekä prosessin nopeuttamiseksi. Tässä työssä tutkittiin näytekirjastoja, jotka sisälsivät luonnosta peräisin olevia uutteita ja puhdasaineita tai synteettisiä yhdisteitä. Näytekirjastoista seulottiin yhdisteitä, jotka estävät ensisijaisesti asetyylikoliiniesteraasia ja toissijaisesti butyryylikoliiniesteraasia. Työssä arvioitiin myös kahden kokeen toimivuutta ja käyttökelpoisuutta seulonnassa. Lisäksi kokeet muokattiin sopimaan monentyyppisten näytekirjastojen seulontaan, koska uutekirjastojen ja puhdasainekirjastojen ominaisuudet asettavat erilaisia haasteita koejärjestelyille. Kokeiden validoinnilla varmistettiin, että seulonnassa saatavista tuloksista tulee luotettavia. Työssä löydettiin useita koliiniesteraasien estäjiä, joiden estovaikutukset erosivat toisistaan selektiivisyyden ja tehon osalta. Uusia koliiniesteraasien estäjiä löytyi sekä luonnonkirjastoista että synteettisestä kirjastosta, mikä osoittaa näiden lähteiden täydentävän toisiaan lääkkeenkeksimisprosessissa. On yleisesti tiedossa, että luonnosta peräisin olevat yhdisteet eroavat rakenteellisesti synteettisten näytekirjastojen yhdisteistä. Molempia lähteitä kannattaa hyödyntää lääkkeenkeksimisprosessissa, erityisesti rakenteellisen monimuotoisuuden ollessa seulontaan valittavien yhdisteiden kriteeri

Identification of Potential Insect Growth Inhibitor against Aedes aegypti: A Bioinformatics Approach

Aedes aegypti is the main vector that transmits viral diseases such as dengue, hemorrhagic dengue, urban yellow fever, zika, and chikungunya. Worldwide, many cases of dengue have been reported in recent years, showing significant growth. The best way to manage diseases transmitted by Aedes aegypti is to control the vector with insecticides, which have already been shown to be toxic to humans; moreover, insects have developed resistance. Thus, the development of new insecticides is considered an emergency. One way to achieve this goal is to apply computational methods based on ligands and target information. In this study, sixteen compounds with acceptable insecticidal activities, with 100% larvicidal activity at low concentrations (2.0 to 0.001 mg center dot L-1), were selected from the literature. These compounds were used to build up and validate pharmacophore models. Pharmacophore model 6 (AUC = 0.78; BEDROC = 0.6) was used to filter 4793 compounds from the subset of lead-like compounds from the ZINC database; 4142 compounds (dG < 0 kcal/mol) were then aligned to the active site of the juvenile hormone receptor Aedes aegypti (PDB: 5V13), 2240 compounds (LE < -0.40 kcal/mol) were prioritized for molecular docking from the construction of a chitin deacetylase model of Aedes aegypti by the homology modeling of the Bombyx mori species (PDB: 5ZNT), which aligned 1959 compounds (dG < 0 kcal/mol), and 20 compounds (LE < -0.4 kcal/mol) were predicted for pharmacokinetic and toxicological prediction in silico (Preadmet, SwissADMET, and eMolTox programs). Finally, the theoretical routes of compounds M01, M02, M03, M04, and M05 were proposed. Compounds M01-M05 were selected, showing significant differences in pharmacokinetic and toxicological parameters in relation to positive controls and interaction with catalytic residues among key protein sites reported in the literature. For this reason, the molecules investigated here are dual inhibitors of the enzymes chitin synthase and juvenile hormonal protein from insects and humans, characterizing them as potential insecticides against the Aedes aegypti mosquito.Laboratory of Cellular Immunology Applied to Health of the Oswaldo Cruz Foundation (FIOCRUZ)Department of Pharmaceutical and Organic Chemistry, Faculty of Pharmacy of the University of Granada (Spain)Researcher Assistance Program-PAPESQ/UNIFA

A perspective on multi-target drug discovery and design for complex diseases

Diseases of infection, of neurodegeneration (such as Alzheimer's and Parkinson's diseases), and of malignancy (cancers) have complex and varied causative factors. Modern drug discovery has the power to identify potential modulators for multiple targets from millions of compounds. Computational approaches allow the determination of the association of each compound with its target before chemical synthesis and biological testing is done. These approaches depend on the prior identification of clinically and biologically validated targets. This Perspective will focus on the molecular and computational approaches that underpin drug design by medicinal chemists to promote understanding and collaboration with clinical scientists