The Structure of Murine N1-Acetylspermine Oxidase Reveals Molecular Details of Vertebrate Polyamine Catabolism.

N1-Acetylspermine oxidase (APAO) catalyzes the conversion of N1-acetylspermine or N1-acetylspermidine to spermidine or putrescine, respectively, with concomitant formation of N-acetyl-3-aminopropanal and hydrogen peroxide. Here we present the structure of murine APAO in its oxidized holo form and in complex with substrate. The structures provide a basis for understanding molecular details of substrate interaction in vertebrate APAO, highlighting a key role for an asparagine residue in coordinating the N1-acetyl group of the substrate. We applied computational methods to the crystal structures to rationalize previous observations with regard to the substrate charge state. The analysis suggests that APAO features an active site ideally suited for binding of charged polyamines. We also reveal the structure of APAO in complex with the irreversible inhibitor MDL72527. In addition to the covalent adduct, a second MDL72527 molecule is bound in the active site. Binding of MDL72527 is accompanied by altered conformations in the APAO backbone. On the basis of structures of APAO, we discuss the potential for development of specific inhibitors.

Molecular properties determining unbound intracellular and extracellular brain exposure of CNS drug candidates.

In the present work we sought to gain a mechanistic understanding of the physicochemical properties that influence the transport of unbound drug across the blood-brain barrier (BBB) as well as the intra- and extracellular drug exposure in the brain. Interpretable molecular descriptors that significantly contribute to the three key neuropharmacokinetic properties related to BBB drug transport (Kp,uu,brain), intracellular accumulation (Kp,uu,cell), and binding and distribution in the brain (Vu,brain) for a set of 40 compounds were identified using partial least-squares (PLS) analysis. The tailoring of drug properties for improved brain exposure includes decreasing the polarity and/or hydrogen bonding capacity. The design of CNS drug candidates with intracellular targets may benefit from an increase in basicity and/or the number of hydrogen bond donors. Applying this knowledge in drug discovery chemistry programs will allow designing compounds with more desirable CNS pharmacokinetic properties.

A protein tertiary structure mimetic modulator of the Hippo signalling pathway.

Transcription factors are key protein effectors in the regulation of gene transcription, and in many cases their activity is regulated via a complex network of protein-protein interactions (PPI). The chemical modulation of transcription factor activity is a long-standing goal in drug discovery but hampered by the difficulties associated with the targeting of PPIs, in particular when extended and flat protein interfaces are involved. Peptidomimetics have been applied to inhibit PPIs, however with variable success, as for certain interfaces the mimicry of a single secondary structure element is insufficient to obtain high binding affinities. Here, we describe the design and characterization of a stabilized protein tertiary structure that acts as an inhibitor of the interaction between the transcription factor TEAD and its co-repressor VGL4, both playing a central role in the Hippo signalling pathway. Modification of the inhibitor with a cell-penetrating entity yielded a cell-permeable proteomimetic that activates cell proliferation via regulation of the Hippo pathway, highlighting the potential of protein tertiary structure mimetics as an emerging class of PPI modulators.

Molecular characteristics for solid-state limited solubility.

Solubility and solid-state characteristics were determined and multivariate data analysis was used to deduce structural features important for solid-state limited solubility of marketed drugs. Molecules with extended ring structures and large conjugated systems were less soluble, indicating that structural features related to rigidity and aromaticity result in solubility restricted by stable crystal structures. These descriptors successfully predicted the applied test set and can be useful for avoiding synthesis of compounds behaving like "brick dust".

Poorly soluble marketed drugs display solvation limited solubility.

We determined the intrinsic aqueous solubility of 15 poorly soluble drugs with solubilities ranging from 2.9 nM to 1.1 microM. We then analyzed the data from a physicochemical perspective, using experimentally determined solid-state properties and easily interpretable two-dimensional molecular descriptors, to better understand the factors underlying poor solubility. The analysis shows that poorly soluble drugs that have reached the market are solubility limited by solvation rather than by their solid state.

Contribution of solid-state properties to the aqueous solubility of drugs.

This study investigates the influence of the solid-state properties melting point (T(m)), enthalpy of melting (DeltaH(m)) and entropy of melting (DeltaS(m)) of a drug on its intrinsic solubility (S(0)). For this purpose, 26 chemically and structurally diverse drugs covering the oral drug space were selected and the S(0), T(m), DeltaH(m) and DeltaS(m) were determined experimentally. The influence of T(m), DeltaH(m) and DeltaS(m) on S(0) was studied using regression analysis. The overall improvement of the predictions were 0.3 log units, however, five compounds (astemizole, glyburide, fenbufen, gliclazide and griseofulvin) were improved by more than one log unit. T(m) and DeltaH(m) had a larger effect than DeltaS(m) on the solubility predictions. The well-known general solubility equation (GSE) and the Dannenfelser semi-empirical equation for the calculation of DeltaS(m) were evaluated using our data set. While predictions of drug solubility obtained using the GSE were acceptable, the use of the experimental DeltaS(m) values instead of the constant 56.5 J mol(-1)K(-1) improved the accuracy of the prediction. The Dannenfelser equation underestimated the DeltaS(m) for most compounds with on average 15 J mol(-1)K(-1). Our results show that solid-state properties should be considered for improved performance of future models for prediction of drug solubility. In addition our study provides accurate experimental data on intrinsic solubility for 26 compounds, supplying a useful external data set for validation of drug solubility models.

Dihydrothiazolopyridone derivatives as a novel family of positive allosteric modulators of the metabotropic glutamate 5 (mGlu5) receptor.

Starting from a singleton chromanone high throughput screening (HTS) hit, we describe a focused medicinal chemistry optimization effort leading to the identification of a novel series of phenoxymethyl-dihydrothiazolopyridone derivatives as selective positive allosteric modulators (PAMs) of the metabotropic glutamate 5 (mGlu5) receptor. These dihydrothiazolopyridones potentiate receptor responses in recombinant systems. In vitro and in vivo drug metabolism and pharmacokinetic (DMPK) evaluation allowed us to select compound 16a for its assessment in a preclinical animal screen of possible antipsychotic activity. 16a was able to reverse amphetamine-induced hyperlocomotion in rats in a dose-dependent manner without showing any significant motor impairment or overt neurological side effects at comparable doses. Evolution of our medicinal chemistry program, structure activity, and properties relationships (SAR and SPR) analysis as well as a detailed profile for optimized mGlu5 receptor PAM 16a are described.

Global and local computational models for aqueous solubility prediction of drug-like molecules.

The aim of this study was to develop in silico protocols for the prediction of aqueous drug solubility. For this purpose, high quality solubility data of 85 drug-like compounds covering the total drug-like space as identified with the ChemGPS methodology were used. Two-dimensional molecular descriptors describing electron distribution, lipophilicity, flexibility, and size were calculated by Molconn-Z and Selma. Global minimum energy conformers were obtained by Monte Carlo simulations in MacroModel and three-dimensional descriptors of molecular surface area properties were calculated by Marea. PLS models were obtained by use of training and test sets. Both a global drug solubility model (R(2) = 0.80, RMSE(te) = 0.83) and subset specific models (after dividing the 85 compounds into acids, bases, ampholytes, and nonproteolytes) were generated. Furthermore, the final models were successful in predicting the solubility values of external test sets taken from the literature. The results showed that homologous series and subsets can be predicted with high accuracy from easily comprehensible models, whereas consensus modeling might be needed to predict the aqueous drug solubility of datasets with large structural diversity.