Impact of Replacement of Individual Dietary SFAs on Circulating Lipids and Other Biomarkers of Cardiometabolic Health: A Systematic Review and Meta-Analysis of Randomized Controlled Trials in Humans.

Little is known of the impact of individual SFAs and their isoenergetic substitution with other SFAs or unsaturated fatty acids (UFAs) on the prevention of cardiometabolic disease (CMD). This systematic literature review assessed the impact of such dietary substitutions on a range of fasting CMD risk markers, including lipid profile, markers of glycemic control and inflammation, and metabolic hormone concentrations. Eligible randomized controlled trials (RCTs) investigated the effect of isoenergetic replacements of individual dietary SFAs for ≥14 d on ≥1 CMD risk markers in humans. Searches of the PubMed, Embase, Scopus, and Cochrane CENTRAL databases on 14 February, 2021 identified 44 RCTs conducted in participants with a mean ± SD age of 39.9 ± 15.2 y. Studies' risk of bias was assessed using the Cochrane Risk of Bias tool 2.0 for RCTs. Random-effect meta-analyses assessed the effect of ≥3 similar dietary substitutions on the same CMD risk marker. Other dietary interventions were described in qualitative syntheses. We observed reductions in LDL-cholesterol concentrations after the replacement of palmitic acid (16:0) with UFAs (-0.36 mmol/L; 95% CI: -0.50, -0.21 mmol/L; I2 = 96.0%, n = 18 RCTs) or oleic acid (18:1n-9) (-0.16 mmol/L; 95% CI: -0.28, -0.03 mmol/L; I2 = 89.6%, n = 9 RCTs), with a similar impact on total cholesterol and apoB concentrations. No effects on other CMD risk markers, including HDL-cholesterol, triacylglycerol, glucose, insulin, or C-reactive protein concentrations, were evident. Similarly, we found no evidence of a benefit from replacing dietary stearic acid (18:0) with UFAs on CMD risk markers (n = 4 RCTs). In conclusion, the impact of replacing dietary palmitic acid with UFAs on lipid biomarkers is aligned with current public health recommendations. However, owing to the high heterogeneity and limited studies, relations between all individual SFAs and biomarkers of cardiometabolic health need further confirmation from RCTs. This systematic review was registered at www.crd.york.ac.uk/prospero/ as CRD42020084241.

Molecule Ideation Using Matched Molecular Pairs.

Matched Molecular Pair Analysis (MMP) is a very important tool during the lead optimization stage in drug discovery. The usefulness of this tool in the lead optimization stage has been discussed in several peer-reviewed articles. The application of MMP in Molecule generation is relatively new. This brings several challenges one of them being the need to encode contextual information into the transforms. In this chapter, we discuss how we use MMPs as a molecule generation method and how does it compare with other molecular generators.

Discovery of Sisunatovir (RV521), an Inhibitor of Respiratory Syncytial Virus Fusion.

RV521 is an orally bioavailable inhibitor of respiratory syncytial virus (RSV) fusion that was identified after a lead optimization process based upon hits that originated from a physical property directed hit profiling exercise at Reviral. This exercise encompassed collaborations with a number of contract organizations with collaborative medicinal chemistry and virology during the optimization phase in addition to those utilized as the compound proceeded through preclinical and clinical evaluation. RV521 exhibited a mean IC50 of 1.2 nM against a panel of RSV A and B laboratory strains and clinical isolates with antiviral efficacy in the Balb/C mouse model of RSV infection. Oral bioavailability in preclinical species ranged from 42 to >100% with evidence of highly efficient penetration into lung tissue. In healthy adult human volunteers experimentally infected with RSV, a potent antiviral effect was observed with a significant reduction in viral load and symptoms compared to placebo.

The Derivation of a Matched Molecular Pairs Based ADME/Tox Knowledge Base for Compound Optimization.

Matched Molecular Pairs (MMP) analysis is a well-established technique for Structure Activity and Property Analysis (SAR and SPR). Summarizing multiple MMPs that describe the same structural change into a single chemical transform can be a powerful tool for prediction (termed Transform from here on). This is particularly useful in the area of Absorption, Distribution, Metabolism, and Elimination (ADME) analysis that is less influenced by 3D structural binding effects. The creation of a knowledge database containing many of these Transforms across typical ADME assays promises to be a powerful approach to aid multidimensional optimization. We present a detailed workflow for the derivation of such a database. We include details of an MMP fragmentation algorithm with associated statistical summarization methods for the derivation of Transforms. This is made freely available as part of the LillyMol software package. We describe the application of this method to several ADME/Tox (Toxicity) assay data sets and highlight multiple cases where the impact of traditional medicinal chemistry Transforms is contradicted by MMP data. We also describe the internal software interface used by medicinal chemists to aid the design of new compounds via automated suggestion. This approach utilizes the matched pairs database to "suggest" improved compounds in an automated design scenario. A nonvisual script-based version of the automated suggestions code with an associated set of described chemical Transforms is also made freely available along with this paper and as part of the LillyMol software package. Finally, we contrast this knowledge database against a larger database of all MMPs derived from a 2 million compound diversity set and a subset of MMPs seen in historical discovery projects. The comparison against all transforms in the diversity collection highlights the very low coverage of the transform database as compared to all possible transforms involving 15 atom fragments. The comparison against a smaller subset of Transforms seen on internal Medicinal Chemistry projects shows better coverage of the transform database for a small set of common medicinal chemistry strategies. Within the context of all possible transforms available to a medicinal chemistry project team, the challenge remains to move beyond mere idea generation from past projects toward high quality prediction for novel ADME/Tox modulating Transforms.

A KNIME Workflow for Automated Structure Verification.

Adequate characterization of chemical entities made for biological screening in the drug discovery context is critical. Incorrectly characterized structures lead to mistakes in the interpretation of structure-activity relationships and confuse an already multidimensional optimization problem. Mistakes in the later use of these compounds waste money and valuable resources in a discovery process already under cost pressure. Left unidentified, these errors lead to problems in project data packages during quality review. At worst, they put intellectual property and patent integrity at risk. We describe a KNIME workflow for the early and automated identification of these errors during registration of a new chemical entity into the corporate screening catalog. This Automated Structure Verification workflow provides early identification (within 24 hours) of missing or inconsistent analytical data and therefore reduces any mistakes that inevitably get made. Automated identification removes the burden of work from the chemist submitting the compound into the registration system. No additional work is required unless a problem is identified and the submitter alerted. Before implementation, 14% of samples within the existing sample catalog were missing data on initial pass. A year after implementation, only 0.2% were missing data.

1,4-benzodiazepines as inhibitors of respiratory syncytial virus. The identification of a clinical candidate.

Respiratory syncytial virus (RSV) is the cause of one-fifth of all lower respiratory tract infections worldwide and is increasingly being recognized as representing a serious threat to patient groups with poorly functioning or immature immune systems. Racemic 1,4-benzodiazepines show potent anti-RSV activity in vitro. Anti-RSV evaluation of 3-position R- and S-benzodiazepine enantiomers and subsequent optimization of this series resulted in selection of a clinical candidate. Antiviral activity was found to reside mainly in the S-enantiomer, and the R-enantiomers were consistently less active against RSV. Analogues of 1,4-(S)-benzodiazepine were synthesized as part of the lead optimization program at Arrow and tested in the XTT assay. From this exercise, (S)-1-(2-fluorophenyl)-3-(2-oxo-5-phenyl-2,3-dihydro-1H-benzo[e][1,4]-diazepin-3-yl)-urea, 17b (RSV-604) was identified as a clinical candidate, exhibiting potent anti-RSV activity in the XTT assay, which was confirmed in secondary assays. Compound 17b also possessed a good pharmacokinetic profile and has now progressed into the clinic.

Scoring functions and enrichment: a case study on Hsp90.

BACKGROUND: The need for fast and accurate scoring functions has been driven by the increased use of in silico virtual screening twinned with high-throughput screening as a method to rapidly identify potential candidates in the early stages of drug development. We examine the ability of some the most common scoring functions (GOLD, ChemScore, DOCK, PMF, BLEEP and Consensus) to discriminate correctly and efficiently between active and non-active compounds among a library of approximately 3,600 diverse decoy compounds in a virtual screening experiment against heat shock protein 90 (Hsp90). RESULTS: Firstly, we investigated two ranking methodologies, GOLDrank and BestScorerank. GOLDrank is based on ranks generated using GOLD. The various scoring functions, GOLD, ChemScore, DOCK, PMF, BLEEP and Consensus, are applied to the pose ranked number one by GOLD for that ligand. BestScorerank uses multiple poses for each ligand and independently chooses the best ranked pose of the ligand according to each different scoring function. Secondly, we considered the effect of introducing the Thr184 hydrogen bond tether to guide the docking process towards a particular solution, and its effect on enrichment. Thirdly, we considered normalisation to account for the known bias of scoring functions to select larger molecules. All the scoring functions gave fairly similar enrichments, with the exception of PMF which was consistently the poorest performer. In most cases, GOLD was marginally the best performing individual function; the Consensus score usually performed similarly to the best single scoring function. Our best results were obtained using the Thr184 tether in combination with the BestScorerank protocol and normalisation for molecular weight. For that particular combination, DOCK was the best individual function; DOCK recovered 90% of the actives in the top 10% of the ranked list; Consensus similarly recovered 89% of the actives in its top 10%. CONCLUSION: Overall, we demonstrate the validity of virtual screening as a method for identifying new leads from a pool of ligands with similar physicochemical properties and we believe that the outcome of this study provides useful insight into the setting up of a suitable docking and scoring protocol, resulting in enrichment of 'target active' compounds.