Open PHACTS computational protocols for in silico target validation of cellular phenotypic screens: knowing the knowns.

Phenotypic screening is in a renaissance phase and is expected by many academic and industry leaders to accelerate the discovery of new drugs for new biology. Given that phenotypic screening is per definition target agnostic, the emphasis of in silico and in vitro follow-up work is on the exploration of possible molecular mechanisms and efficacy targets underlying the biological processes interrogated by the phenotypic screening experiments. Herein, we present six exemplar computational protocols for the interpretation of cellular phenotypic screens based on the integration of compound, target, pathway, and disease data established by the IMI Open PHACTS project. The protocols annotate phenotypic hit lists and allow follow-up experiments and mechanistic conclusions. The annotations included are from ChEMBL, ChEBI, GO, WikiPathways and DisGeNET. Also provided are protocols which select from the IUPHAR/BPS Guide to PHARMACOLOGY interaction file selective compounds to probe potential targets and a correlation robot which systematically aims to identify an overlap of active compounds in both the phenotypic as well as any kinase assay. The protocols are applied to a phenotypic pre-lamin A/C splicing assay selected from the ChEMBL database to illustrate the process. The computational protocols make use of the Open PHACTS API and data and are built within the Pipeline Pilot and KNIME workflow tools.

Valerenic acid derivatives as novel subunit-selective GABAA receptor ligands - in vitro and in vivo characterization.

BACKGROUND AND PURPOSE: Subunit-specific modulators of gamma-aminobutyric acid (GABA) type A (GABA(A)) receptors can help to assess the physiological function of receptors with different subunit composition and also provide the basis for the development of new drugs. Valerenic acid (VA) was recently identified as a beta(2/3) subunit-specific modulator of GABA(A) receptors with anxiolytic potential. The aim of the present study was to generate VA derivatives as novel GABA(A) receptor modulators and to gain insight into the structure-activity relation of this molecule. EXPERIMENTAL APPROACH: The carboxyl group of VA was substituted by an uncharged amide or amides with different chain length. Modulation of GABA(A) receptors composed of different subunit compositions by the VA derivatives was studied in Xenopus oocytes by means of the two-microelectrode voltage-clamp technique. Half-maximal stimulation of GABA-induced chloride currents (I(GABA)) through GABA(A) receptors (EC(50)) and efficacies (maximal stimulation of I(GABA)) were estimated. Anxiolytic activity of the VA derivatives was studied in mice, applying the elevated plus maze test. KEY RESULTS: Valerenic acid amide (VA-A) displayed the highest efficacy (more than twofold greater I(GABA) enhancement than VA) and highest potency (EC(50)= 13.7 +/- 2.3 microM) on alpha(1)beta(3) receptors. Higher efficacy and potency of VA-A were also observed on alpha(1)beta(2)gamma(2s) and alpha(3)beta(3)gamma(2s) receptors. Anxiolytic effects were most pronounced for VA-A. CONCLUSIONS AND IMPLICATIONS: Valerenic acid derivatives with higher efficacy and affinity can be generated. Greater in vitro action of the amide derivative correlated with a more pronounced anxiolytic effect in vivo. The data give further confidence in targeting beta(3) subunit containing GABA(A) receptors for development of anxiolytics.

Pharmacoinformatic approaches to design natural product type ligands of ABC-transporters.

ABC-transporter have been recognized as being responsible for multiple drug resistance in tumor therapy, for decreased brain uptake and low oral bioavailability of drug candidates, and for drug-drug interactions and drug induced cholestasis. P-glycoprotein (ABCB1), the paradigm protein in the field, is mainly effluxing natural product toxins and shows very broad substrate specificity. Within this article we will highlight SAR and QSAR approaches for designing natural product type inhibitors of ABCB1 and related proteins as well as in silico strategies to predict ABCB1 substrates and inhibitors in order to design out undesirable drug/protein interaction.

Future directions for drug transporter modelling.

Since the late 1980s computational methods such as quantitative structure-activity relationship (QSAR) and pharmacophore approaches have become more widely applied to assess interactions between drug-like molecules and transporters, starting with P-glycoprotein (P-gp). Identifying molecules that interact with P-gp and other transporters is important for drug discovery, but it is normally ascertained using laborious in vitro and in vivo studies. Computational QSAR and pharmacophore models can be used to screen commercial databases of molecules rapidly and suggest those likely to bind as substrates or inhibitors for transporters. These predictions can then be readily verified in vitro, thus representing a more efficient route to screening. Recently, the application of this approach has seen the identification of new substrates and inhibitors for several transporters. The successful application of computational models and pharmacophore models in particular to predict transporter binding accurately represents a way to anticipate drug-drug interactions of novel molecules from molecular structure. These models may also see incorporation in future pharmacokinetic-pharmacodynamic models to improve predictions of in vivo drug effects in patients. The implications of early assessment of transporter activity, current advances in QSAR, and other computational methods for future development of these and systems-based approaches will be discussed.

Interaction field based and hologram based QSAR analysis of propafenone-type modulators of multidrug resistance.

Overexpression of membrane bound, ATP-dependent transport proteins is one of the predominant mechanisms leading to multiple drug resistance in tumor therapy as well as in the treatment of bacterial and fungal infections. In tumor therapy, P-glycoprotein (P-gp, ABCB1) is responsible for transport of a wide variety of natural product toxins out of tumor cells leading to decreased accumulation of cytotoxic drugs within the cells. Inhibition of P-gp thus gives rise to a resensitization of multidrug resistant tumor cells and represents a versatile approach for modulation of multidrug resistance. Within this paper, a set of propafenone-type inhibitors of P-gp were analyzed using both interaction field based methods such as CoMFA and CoMSIA and Hologram QSAR. With both methods, highly predictive models with q2-values>0.65 were obtained. Models using logP as additional descriptor generally yielded higher predictive power. On basis of unfavorable steric and favorable electrostatic and hydrophobic interaction fields, these models were able to explain all outlayers identified in previous Hansch-analyses. For HQSAR analysis, models with q2-values up to 0.72 were obtained. Positive influences were found for electron donating groups on the aromatic systems. Highly negative influences were found for diphenylalkylamine substituents, which is a further hint for steric hindrance. The models with highest predictive power were used for screening of a small virtual library. Synthesis and pharmacological testing of a sub set of this library showed that the external predictivity of the HQSAR models generally is lower than the internal one.