With the goal of understanding how the nature of the tridentate macrocyclic supporting ligand influences the relative stability of isomeric mu-eta 2:eta 2-peroxo- and bis(mu-oxo)dicopper complexes, a comparative study was undertaken of the O2 reactivity of Cu(I) compounds supported by the 10- and 12-membered macrocycles, 1,4,7-R3-1,4,7-triazacyclodecane (R3TACD; R = Me, Bn, iPr) and 1,5,9-triisopropyl-1,5,9-triazacyclododecane (iPr3TACDD). While the 3-coordinate complex [(iPr3TACDD)Cu]SbF6 was unreactive with O2, oxygenation of [(R3TACD)Cu(CH3CN)]X (R = Me or Bn; X = ClO4- or SbF6-) at -80 degrees C yielded bis(mu-oxo) species [(R3TACD)2Cu2(mu O)2]X2 as revealed by UV-vis and resonance Raman spectroscopy. Interestingly, unlike the previously reported system supported by 1,4,7-triisopropyl-1,4,7-triazacyclononane (iPr3TACN), which yielded interconverting mixtures of peroxo and bis(mu-oxo) compounds (Cahoy, J.; Holland, P. L.; Tolman, W. B. Inorg. Chem. 1999, 38, 2161), low-temperature oxygenation of [(iPr3TACD)Cu(CH3CN)]SbF6 in a variety of solvents cleanly yielded a mu-eta 2:eta 2-peroxo product, with no trace of the bis(mu-oxo) isomer. The peroxo complex was characterized by UV-vis and resonance Raman spectroscopy, as well as an X-ray crystal structure (albeit of marginal quality due to disorder problems). Intramolecular attack at the alpha C-H bonds of the substituents was indicated as the primary decomposition pathway of the oxygenated compounds through examination of the decay kinetics and the reaction products, which included bis(mu-hydroxo)- and mu-carbonato-dicopper complexes that were characterized by X-ray diffraction. A rationale for the varying results of the oxygenation reactions was provided by analysis of (a) the X-ray crystal structures and electrochemical behavior of the Cu(I) precursors and (b) the results of theoretical calculations of the complete oxygenated complexes, including all ligand atoms, using combined quantum chemical/molecular mechanics (integrated molecular orbital molecular mechanics, IMOMM) methods. The size of the ligand substituents was shown to be a key factor in controlling the relative stabilities of the peroxo and bis(mu-oxo) forms, and the nature of this influence was shown by both theory and experiment to depend on the ligand macrocycle ring size.

We developed highly predictive classification models for human liver microsomal (HLM) stability using the apparent intrinsic clearance (CL(int, app)) as the end point. HLM stability has been shown to be an important factor related to the metabolic clearance of a compound. Robust in silico models that predict metabolic clearance are very useful in early drug discovery stages to optimize the compound structure and to select promising leads to avoid costly drug development failures in later stages. Using Random Forest and Bayesian classification methods with MOE, E-state descriptors, ADME Keys, and ECFP_6 fingerprints, various highly predictive models were developed. The best performance of the models shows 80 and 75% prediction accuracy for the test and validation sets, respectively. A detailed analysis of results will be shown, including an assessment of the prediction confidence, the significant descriptors, and the application of these models to drug discovery projects.

Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.

Modeling and simulation (M&S) techniques provide a platform for knowledge integration and hypothesis testing to gain insights into biological systems that would not be possible a priori. Agent‐based modeling (ABM) is an M&S technique that focuses on describing individual components rather than homogenous populations. This tutorial introduces ABM to systems pharmacologists, using relevant case studies to highlight how ABM‐specific strengths have yielded success in the area of preclinical mechanistic modeling.