This paper brings together the concepts of molecular complexity and crowdsourcing. An exercise was done at Merck where 386 chemists voted on the molecular complexity (on a scale of 1-5) of 2681 molecules taken from various sources: public, licensed, and in-house. The meanComplexity of a molecule is the average over all votes for that molecule. As long as enough votes are cast per molecule, we find meanComplexity is quite easy to model with QSAR methods using only a handful of physical descriptors (e.g., number of chiral centers, number of unique topological torsions, a Wiener index, etc.). The high level of self-consistency of the model (cross-validated R(2) ∼0.88) is remarkable given that our chemists do not agree with each other strongly about the complexity of any given molecule. Thus, the power of crowdsourcing is clearly demonstrated in this case. The meanComplexity appears to be correlated with at least one metric of synthetic complexity from the literature derived in a different way and is correlated with values of process mass intensity (PMI) from the literature and from in-house studies. Complexity can be used to differentiate between in-house programs and to follow a program over time.

Interleukin-23 (IL-23) is essential for the differentiation of pathogenic effector T helper 17 (Th17) cells, but its role in memory Th17 cell responses is unclear. Using the experimental autoimmune encephalomyelitis (EAE) model, we report that memory Th17 cells rapidly expanded in response to rechallenge and migrated to the CNS in high numbers, resulting in earlier onset and increased severity of clinical disease. Memory Th17 cells were generated from IL-17+ and RORγt+ precursors, and the stability of the Th17 cell phenotype depended on the amount of time allowed for the primary response. IL-23 was required for this enhanced recall response. IL-23 receptor blockade did not directly impact IL-17 production, but did impair the subsequent proliferation and generation of effectors coexpressing the Th1 cell-specific transcription factor T-bet. In addition, many genes required for cell-cycle progression were downregulated in Th17 cells that lacked IL-23 signaling, showing that a major mechanism for IL-23 in primary and memory Th17 cell responses operates via regulation of proliferation-associated pathways.

Thymic stromal lymphopoietin (TSLP) plays an important role in allergic diseases and is highly expressed in keratinocytes in human lesional atopic dermatitis (AD) skin. In nonlesional AD skin TSLP expression can be induced by applying house dust mite allergen onto the skin in the atopy patch test. Several studies have demonstrated that the induction of TSLP expression in mouse skin does not only lead to AD-like inflammation of the skin, but also predisposes to severe inflammation of the airways. In mice, TSLP expression can be induced by application of the 1,25-dihydroxyvitamin D3 (VD3) analogue calcipotriol and results in the development of eczema-like lesions. The objective is to investigate the effect of VD3 (calcitriol) or calcipotriol on TSLP expression in normal human skin and skin from AD patients. Using multiple ex vivo experimental setups, the effects of calci(po)triol on TSLP expression by normal human skin, and skin from AD patients were investigated and compared to effects of calcipotriol on mouse and non-human primates (NHP) skin. No induction of TSLP expression (mRNA or protein) was observed in human keratinocytes, normal human skin, nonlesional AD skin, or NHP skin samples after stimulation with calcipotriol or topical application of calcitriol. The biological activity of calci(po)triol in human skin samples was demonstrated by the increased expression of the VD3-responsive Cyp24a1 gene. TSLP expression was induced by cytokines (IL-4, IL-13, and TNF-α) in skin samples from all three species. In contrast to the findings in human and NHP, a consistent increase in TSLP expression was confirmed in mouse skin biopsies after stimulation with calcipotriol. VD3 failed to induce expression of TSLP in human or monkey skin in contrast to mouse, implicating careful extrapolation of this often-used mouse model to AD patients.

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Therapeutic peptides offer potential advantages over small molecules in terms of selectivity, affinity, and their ability to target "undruggable" proteins that are associated with a wide range of pathologies. Despite their importance, current molecular design capabilities that inform medicinal chemistry decisions on peptide programs are limited. More specifically, there are unmet needs for structure−activity relationship (SAR) analysis and visualization of linear, cyclic, and cross-linked peptides containing nonnatural motifs, which are widely used in drug discovery. To bridge this gap, we developed PepSeA (Peptide Sequence Alignment and Visualization), an open-source, freely available package of sequence-based tools (https://github.com/Merck/PepSeA). PepSeA enables multiple sequence alignment of non-natural amino acids and enhanced visualization with the hierarchical editing language for macromolecules (HELM). Via stepwise SAR analysis of a ChEMBL peptide data set, we demonstrate the utility of PepSeA to accelerate decision making in lead optimization campaigns in pharmaceutical setting. PepSeA represents an initial attempt to expand cheminformatics capabilities for therapeutic peptides and to enable rapid and more efficient design-make-test cycles.