The use of 2D fingerprint methods to support the assessment of structural similarity in orphan drug legislation.

In the European Union, medicines are authorised for some rare disease only if they are judged to be dissimilar to authorised orphan drugs for that disease. This paper describes the use of 2D fingerprints to show the extent of the relationship between computed levels of structural similarity for pairs of molecules and expert judgments of the similarities of those pairs. The resulting relationship can be used to provide input to the assessment of new active compounds for which orphan drug authorisation is being sought

Truncated and Helix-Constrained Peptides with High Affinity and Specificity for the cFos Coiled-Coil of AP-1

Protein-based therapeutics feature large interacting surfaces. Protein folding endows structural stability to localised surface epitopes, imparting high affinity and target specificity upon interactions with binding partners. However, short synthetic peptides with sequences corresponding to such protein epitopes are unstructured in water and promiscuously bind to proteins with low affinity and specificity. Here we combine structural stability and target specificity of proteins, with low cost and rapid synthesis of small molecules, towards meeting the significant challenge of binding coiled coil proteins in transcriptional regulation. By iteratively truncating a Jun-based peptide from 37 to 22 residues, strategically incorporating i-->i+4 helix-inducing constraints, and positioning unnatural amino acids, we have produced short, water-stable, alpha-helical peptides that bind cFos. A three-dimensional NMR-derived structure for one peptide (24) confirmed a highly stable alpha-helix which was resistant to proteolytic degradation in serum. These short structured peptides are entropically pre-organized for binding with high affinity and specificity to cFos, a key component of the oncogenic transcriptional regulator Activator Protein-1 (AP-1). They competitively antagonized the cJun–cFos coiled-coil interaction. Truncating a Jun-based peptide from 37 to 22 residues decreased the binding enthalpy for cJun by ~9 kcal/mol, but this was compensated by increased conformational entropy (TDS ≤ 7.5 kcal/mol). This study demonstrates that rational design of short peptides constrained by alpha-helical cyclic pentapeptide modules is able to retain parental high helicity, as well as high affinity and specificity for cFos. These are important steps towards small antagonists of the cJun-cFos interaction that mediates gene transcription in cancer and inflammatory diseases

Bridged beta(3)-Peptide Inhibitors of p53-hDM2 Complexation: Correlation between Affinity and Cell Permeability

β-peptides possess several features that are desirable in peptidomimetics; they are easily synthesized, fold into stable secondary structures in physiologic buffers, and resist proteolysis. They can also bind to a diverse array of proteins to inhibit their interactions with α–helical ligands. β–peptides are not usually cell permeable, however, and this feature limits their utility as research tools and potential therapeutics. Appending an Arg(8) sequence to a β–peptide improves uptake but adds considerable mass. We reported that embedding a small cationic patch within a PPII, α– or β–peptide helix improves uptake without the addition of significant mass. In another mass-neutral strategy, Verdine, Walensky, and others have reported that insertion of a hydrocarbon bridge between the i and i+4 positions of an α–helix also increases cell uptake. Here we describe a series of β–peptides containing diether and hydrocarbon bridges and compare them on the basis of cell uptake and localization, affinities for hDM2, and 14-helix structure. Our results highlight the relative merits of cationic patch and hydrophobic bridge strategies for improving β–peptide uptake and identify a surprising correlation between uptake efficiency and hDM2 affinity

Inside the Mind of a Medicinal Chemist: The Role of Human Bias in Compound Prioritization during Drug Discovery

<div><p>Medicinal chemists’ “intuition” is critical for success in modern drug discovery. Early in the discovery process, chemists select a subset of compounds for further research, often from many viable candidates. These decisions determine the success of a discovery campaign, and ultimately what kind of drugs are developed and marketed to the public. Surprisingly little is known about the cognitive aspects of chemists’ decision-making when they prioritize compounds. We investigate 1) how and to what extent chemists simplify the problem of identifying promising compounds, 2) whether chemists agree with each other about the criteria used for such decisions, and 3) how accurately chemists report the criteria they use for these decisions. Chemists were surveyed and asked to select chemical fragments that they would be willing to develop into a lead compound from a set of ∼4,000 available fragments. Based on each chemist’s selections, computational classifiers were built to model each chemist’s selection strategy. Results suggest that chemists greatly simplified the problem, typically using only 1–2 of many possible parameters when making their selections. Although chemists tended to use the same parameters to select compounds, differing value preferences for these parameters led to an overall lack of consensus in compound selections. Moreover, what little agreement there was among the chemists was largely in what fragments were <em>undesirable</em>. Furthermore, chemists were often unaware of the parameters (such as compound size) which were statistically significant in their selections, and overestimated the number of parameters they employed. A critical evaluation of the problem space faced by medicinal chemists and cognitive models of categorization were especially useful in understanding the low consensus between chemists.</p> </div

The selection characteristics of chemists with high estimated consensus.

<p>The cultural consensus model was applied to a subset of fragments (311) with >75% agreement by chemists. The estimated consensus obtained by this method is plotted against the fraction of fragments passed by chemists for the entire survey. Each shape describes the primary SNB parameter used to reproduce chemists’ selections, and the color depicts the ROC score of naïve Bayesian classifiers built using ECFP4 as a descriptor for each chemist. A subset of high consensus chemists is above the dashed grey line.</p

Examples of selection preferences based on simple physicochemical properties, and the corresponding SNB classifiers.

<p><b>A</b>: Histogram of number of atoms of fragments selected by chemist 3 as good (green) or bad (red) starting points for drug discovery campaigns. Frequencies are normalized by the total number of selected or unselected compounds, respectively. <b>B</b>: Bayesian score versus number of atoms for minimal Bayesian model build for chemist 3. A positive score indicates a favorable number of atoms, while a negative score indicates an unfavorable number of atoms. <b>C</b>: Histogram of molecular polar surface area of fragments selected by chemist 12 as good (green) or bad (red) starting points for drug discovery campaigns. Frequencies are normalized by the total number of selected or unselected compounds, respectively. <b>D</b>: Bayesian score versus molecular polar surface area bins for SNB classifier built for chemist 12.</p

Ring topology SNB classifier comparison between chemists.

<p>The most favorable and unfavorable keys for the RingBonds_AromaticBonds_RingAssemblies (RB_AB_RA ) descriptor model, which measures the number of ring bonds (RB), aromatic bonds (AB), and ring assemblies (RA) present in a compound, were examined. Representative scaffolds that correspond to these keys are depicted, and are clustered based on how chemists viewed them. The Bayes score for each models built on individual chemists for each key is reported in a heat map. The favorable keys receive a positive score, while unfavorable keys receive a negative score.</p

The parameters extracted from the SNB (red) and RF (blue) classifiers are compared with parameters designated as important in chemists’ self-reports (grey).

<p>The primary parameters for the classifiers are depicted as stars, and the secondary parameters are depicted as circles. The one-tailed Fisher exact probability test (<i>p</i>) is reported for each parameter (except chains and charge), indicating that the SNB and RF parameters show agreement with each other, while the self reported parameters are independent of either of the classifier’s parameters.</p