Work absenteeism, disability, and lost wages among patients with acute myeloid leukemia and their caregivers: a cohort study using US administrative claims and productivity data.

OBJECTIVE: We describe the impact of acute myeloid leukemia (AML) diagnosis on workplace absenteeism and disability days among patients and their caregivers. METHODS: This retrospective study included adults with newly diagnosed AML (2009-2019) and adult caregivers of patients with newly diagnosed AML, identified from the US Merative™ MarketScan® Commercial Database. The Merative MarketScan Health and Productivity Management Database provided linked patient-level records of workplace absence and short-term (STD) and long-term disability (LTD) data. Endpoints included workplace absence, STD and LTD for patients and caregivers during 12 months pre-AML (baseline) and ≤3 years' follow-up, and corresponding cost of work loss. RESULTS: Patient workplace absence decreased in the months post-AML diagnosis, but the number of STD and LTD leave days claimed increased significantly by sixfold and fourfold, respectively. The proportion of patients making STD leave claims increased within 4-5 months of diagnosis, while the proportion making LTD leave claims increased significantly starting from month 5. Caregiver workplace absence peaked in the first 2 months post-diagnosis and remained elevated versus baseline throughout the study. CONCLUSION: AML diagnosis leads to workplace absenteeism and increased economic burden for patients with AML and their caregivers.

Impact of COVID-19 on patient and healthcare professional attitudes, beliefs, and behaviors toward the healthcare system and on the dynamics of the healthcare pathway.

BACKGROUND: COVID-19 has dramatically changed how healthcare is delivered and experienced. METHODS: One-on-one interviews and a virtual ethnographic roundtable were conducted among 45 patients, caregivers, and healthcare professionals (HCPs) in 4 therapeutic areas from the United States and Japan: overactive bladder, vasomotor symptoms, prostate cancer, and metastatic urothelial carcinoma. The goal was to identify the impact of COVID-19 on patient/caregiver and HCP attitudes, interactions, beliefs, and behaviors toward the healthcare system and care pathway. RESULTS: Four foundational themes were identified: 1) COVID-19 risk is relative; 2) isolation is collateral damage; 3) telehealth is a parallel universe; and 4) COVID-19 is destabilizing the foundations of healthcare. Numerous insights, influenced by diverse cultural, social, and psychological factors, were identified within each theme. CONCLUSIONS: The impacts of COVID-19 were noticeable at multiple points of care during the "universal" care pathway, including at initial screening, referral to specialists, diagnosis, treatment initiation/surgery, and during ongoing care. Greater appreciation of the short- and long-term impacts of COVID-19 and resulting gaps in care may act as a catalyst for positive change in future patient care.

Molecular mechanics methods for predicting protein-ligand binding.

Ligand binding affinity prediction is one of the most important applications of computational chemistry. However, accurately ranking compounds with respect to their estimated binding affinities to a biomolecular target remains highly challenging. We provide an overview of recent work using molecular mechanics energy functions to address this challenge. We briefly review methods that use molecular dynamics and Monte Carlo simulations to predict absolute and relative ligand binding free energies, as well as our own work in which we have developed a physics-based scoring method that can be applied to hundreds of thousands of compounds by invoking a number of simplifying approximations. In our previous studies, we have demonstrated that our scoring method is a promising approach for improving the discrimination between ligands that are known to bind and those that are presumed not to, in virtual screening of large compound databases. In new results presented here, we explore several improvements to our computational method including modifying the dielectric constant used for the protein and ligand interiors, and empirically scaling energy terms to compensate for deficiencies in the energy model. Future directions for further improving our physics-based scoring method are also discussed.

Virtual ligand screening against Escherichia coli dihydrofolate reductase: improving docking enrichment using physics-based methods.

Motivated by their participation in the McMaster Data-Mining and Docking Competition, the authors developed 2 new computational technologies and applied them to docking against Escherichia coli dihydrofolate reductase: a receptor preparation procedure that incorporates rotamer optimization of side chains and a physics-based rescoring procedure for estimating relative binding affinities of the protein-ligand complexes. Both methods use the same energy function, consisting of the all-atom OPLS-AA force field and a generalized Born solvent model, which treats the protein receptor and small-molecule ligands in a consistent manner. Thus, the energy function is similar to that used in more sophisticated approaches, such as free-energy perturbation and the molecular mechanics Poisson-Boltzmann/surface area, but sampling during the rescoring procedure is limited to simple energy minimization of the ligand. The use of a highly efficient minimization algorithm permitted the authors to apply this rescoring procedure to hundreds of thousands of protein-ligand complexes during the competition, using a modest Linux cluster. To test these methods, they used the 12 competitive inhibitors identified in the training set, plus methotrexate, as positive controls in enrichment studies with both the training and test sets, each containing 50,000 compounds. The key conclusion is that combining the receptor preparation and rescoring methods makes it possible to identify most of the positive controls within the top few tenths of a percent of the rank-ordered training and test set libraries.

Virtual screening against highly charged active sites: identifying substrates of alpha-beta barrel enzymes.

We have developed a virtual ligand screening method designed to help assign enzymatic function for alpha-beta barrel proteins. We dock a library of approximately 19,000 known metabolites against the active site and attempt to identify the relevant substrate based on predicted relative binding free energies. These energies are computed using a physics-based energy function based on an all-atom force field (OPLS-AA) and a generalized Born implicit solvent model. We evaluate the ability of this method to identify the known substrates of several members of the enolase superfamily of enzymes, including both holo and apo structures (11 total). The active sites of these enzymes contain numerous charged groups (lysines, carboxylates, histidines, and one or more metal ions) and thus provide a challenge for most docking scoring functions, which treat electrostatics and solvation in a highly approximate manner. Using the physics-based scoring procedure, the known substrate is ranked within the top 6% of the database in all cases, and in 8 of 11 cases, it is ranked within the top 1%. Moreover, the top-ranked ligands are strongly enriched in compounds with high chemical similarity to the substrate (e.g., different substitution patterns on a similar scaffold). These results suggest that our method can be used, in conjunction with other information including genomic context and known metabolic pathways, to suggest possible substrates or classes of substrates for experimental testing. More broadly, the physics-based scoring method performs well on highly charged binding sites and is likely to be useful in inhibitor docking against polar binding sites as well. The method is fast (<1 min per ligand), due largely to an efficient minimization algorithm based on the truncated Newton method, and thus, it can be applied to thousands of ligands within a few hours on a small Linux cluster.

Competition between intramolecular hydrogen bonds and solvation in phosphorylated peptides: simulations with explicit and implicit solvent.

The atomic-level mechanisms of protein regulation by post-translational phosphorylation remain poorly understood, except in a few well-studied systems. Molecular mechanics simulations can in principle be used to help understand and predict the effects of protein phosphorylation, but the accuracy of the results will of course depend on the quality of the force field parameters for the phosphorylated residues as well as the quality of the solvent model. The phosphorylated residues typically carry a -2 charge at physiological pH; however, the effects of phosphorylation can sometimes be mimicked by substituting Asp or Glu for the phosphorylated residue. Here we examine the suitability of explicit and implicit solvent models for simulating phospho-serine in both the -1 and -2 charge states. Specifically, we simulate a capped phosphorylated peptide, Ace-Gly-Ser-pSer-Ser-Nme, and compare the results to each other and to experimental observables from an NMR experiment. The first major conclusion is that explicit water models (TIP3P, TIP4P and SPC/E) and a Generalized Born implicit solvent model provide reasonable agreement with the experimental observables, given appropriate partial charges for the phosphate group. The Generalized Born results, however, show greater hydrogen bonding propensity than the explicit solvent results. Distance dependent dielectric treatments perform poorly. The second major conclusion is that many ensemble-averaged properties obtained for the phosphopeptide in the -1 and -2 charge states are strikingly similar; the -1 species has a slightly higher propensity to form internal hydrogen bonds. All of the results can be rationalized by quantifying the strength of the P-O/H-N hydrogen bond, which depends on a sensitive balance between strongly favorable charge/dipole and dipole/dipole interactions and strongly unfavorable desolvation.

Multiple "time step" Monte Carlo simulations: application to charged systems with Ewald summation.

Recently, we have proposed an efficient scheme for Monte Carlo simulations, the multiple "time step" Monte Carlo (MTS-MC) [J. Chem. Phys. 117, 8203 (2002)] based on the separation of the potential interactions into two additive parts. In this paper, the structural and thermodynamic properties of the simple point charge water model combined with the Ewald sum are compared for the MTS-MC real-/reciprocal-space split of the Ewald summation and the common Metropolis Monte Carlo method. We report a number of observables as a function of CPU time calculated using MC and MTS-MC. The correlation functions indicate that speedups on the order of 4.5-7.5 can be obtained for systems of 108-500 waters for n=10 splitting parameter.