Multi-state modeling of G-protein coupled receptors at experimental accuracy

The family of G-protein coupled receptors (GPCRs) is one of the largest protein families in the human genome. GPCRs transduct chemical signals from extracellular to intracellular regions via a conformational switch between active and inactive states upon ligand binding. While experimental structures of GPCRs remain limited, high-accuracy computational predictions are now possible with AlphaFold2. However, AlphaFold2 only predicts one state and is biased toward either the active or inactive conformation depending on the GPCR class. Here, a multi-state prediction protocol is introduced that extends AlphaFold2 to predict either active or inactive states at very high accuracy using state-annotated templated GPCR databases. The predicted models accurately capture the main structural changes upon activation of the GPCR at the atomic level. For most of the benchmarked GPCRs (10 out of 15), models in the active and inactive states were closer to their corresponding activation state structures. Median RMSDs of the transmembrane regions were 1.12 Å and 1.41 Å for the active and inactive state models, respectively. The models were more suitable for protein-ligand docking than the original AlphaFold2 models and template-based models. Finally, our prediction protocol predicted accurate GPCR structures and GPCR-peptide complex structures in GPCR Dock 2021, a blind GPCR-ligand complex modeling competition. We expect that high accuracy GPCR models in both activation states will promote understanding in GPCR activation mechanisms and drug discovery for GPCRs. At the time, the new protocol paves the way towards capturing the dynamics of proteins at high-accuracy via machine-learning methods.

Cost-effectiveness of pembrolizumab for the adjuvant treatment of resected high-risk stage III melanoma in the United States

Aims: To evaluate the cost-effectiveness of adjuvant pembrolizumab relative to observation alone following complete resection of high-risk stage III melanoma with lymph node involvement, from a US health system perspective. Materials and methods: A Markov cohort model with four health states (recurrence-free, locoregional recurrence, distant metastases, and death) was developed to estimate costs, life-years, and quality-adjusted life-years (QALYs) associated with pembrolizumab vs observation over a lifetime (46-year) horizon. Using a parametric multi-state modeling approach, transition probabilities starting from recurrence-free were estimated based on patient-level data from KEYNOTE-054 (NCT02362594), a direct head-to-head phase 3 trial. Post-recurrence transition probabilities were informed by real-world retrospective data and clinical trials in advanced melanoma. Health state utilities and adverse event-related disutility were derived from KEYNOTE-054 trial data and published literature. Costs of drug acquisition and administration, adverse events, disease management, and terminal care were estimated in 2018 US dollars. Deterministic and probabilistic sensitivity analyses were conducted to assess robustness. Results: Over a lifetime horizon, adjuvant pembrolizumab and observation were associated with total QALYs of 9.24 and 5.95, total life-years of 10.54 and 7.15, and total costs of $489,820 and $440,431, respectively. The resulting incremental cost-effectiveness ratios (ICERs) for pembrolizumab vs observation were $15,009/QALY and $14,550/life-year. Across the range of input values and assumptions tested in deterministic sensitivity analyses, pembrolizumab ranged from being a dominant strategy to having an ICER of $57,449/QALY vs observation. The ICER was below a willingness-to-pay threshold of $100,000/QALY in 90.2% of probabilistic simulations. Limitations: Long-term extrapolation of outcomes was based on interim results from KEYNOTE-054, with a median follow-up of 15 months. Conclusions: Based on common willingness-to-pay benchmarks, pembrolizumab is highly cost-effective compared with observation alone for the adjuvant treatment of completely resected stage III melanoma in the US.

Multistate Statistical Modeling: A Tool to Build a Lung Cancer Microsimulation Model That Includes Parameter Uncertainty and Patient Heterogeneity

With the shift toward individualized treatment, cost-effectiveness models need to incorporate patient and tumor characteristics that may be relevant to treatment planning. In this study, we used multistate statistical modeling to inform a microsimulation model for cost-effectiveness analysis of individualized radiotherapy in lung cancer. The model tracks clinical events over time and takes patient and tumor features into account. Four clinical states were included in the model: alive without progression, local recurrence, metastasis, and death. Individual patients were simulated by repeatedly sampling a patient profile, consisting of patient and tumor characteristics. The transitioning of patients between the health states is governed by personalized time-dependent hazard rates, which were obtained from multistate statistical modeling (MSSM). The model simulations for both the individualized and conventional radiotherapy strategies demonstrated internal and external validity. Therefore, MSSM is a useful technique for obtaining the correlated individualized transition rates that are required for the quantification of a microsimulation model. Moreover, we have used the hazard ratios, their 95% confidence intervals, and their covariance to quantify the parameter uncertainty of the model in a correlated way. The obtained model will be used to evaluate the cost-effectiveness of individualized radiotherapy treatment planning, including the uncertainty of input parameters. We discuss the model-building process and the strengths and weaknesses of using MSSM in a microsimulation model for individualized radiotherapy in lung cancer.

Approximate Bayesian computation for the natural history of breast cancer, with application to data from a Milan cohort study

We explore models for the natural history of breast cancer, where the main events of interest are the start of asymptomatic detectability of the disease (through screening) and the time of symptomatic detection (through symptoms). We develop several parametric specifications based on a cure rate structure, and present the results of the analysis of data collected as part of a motivating study from Milan. Participants in the study were part of a regional breast cancer screening program, and their ten-year trajectories were obtained from administrative data available from the Italian national health care system. We first present a tractable model for which we develop the likelihood contributions of the observed trajectories and perform maximum likelihood inference on the latent process. Likelihood based inference is not feasible for more flexible models, and we implement approximate Bayesian computation (ABC) for inference. Issues that arise from the use of ABC for model choice and parameter estimation are discussed, including the problem of choosing appropriate summary statistics. The estimated parameters of the underlying disease process allow for the study of the effect of different examination schedules (age range and frequency of screening examinations) on a population of asymptomatic subjects.

Predicting sojourn times across dementia disease stages, institutionalization, and mortality

INTRODUCTION

Inferring the timeline from mild cognitive impairment (MCI) to severe dementia is pivotal for patients, clinicians, and researchers. Literature is sparse and often contains few patients. We aim to determine the time spent in MCI, mild-, moderate-, severe dementia, and institutionalization until death.

METHODS

Multistate modeling with Cox regression was used to obtain the sojourn time. Covariates were age at baseline, sex, amyloid status, and Alzheimer's disease (AD) or other dementia diagnosis. The sample included a register (SveDem) and memory clinics (Amsterdam Dementia Cohort and Memento).

RESULTS

Using 80,543 patients, the sojourn time from clinically identified MCI to death across all patient groups ranged from 6.20 (95% confidence interval [CI]: 5.57-6.98) to 10.08 (8.94-12.18) years.

DISCUSSION

Generally, sojourn time was inversely associated with older age at baseline, males, and AD diagnosis. The results provide key estimates for researchers and clinicians to estimate prognosis.

Estimation in discrete time coarsened multivariate longitudinal models

We consider the analysis of longitudinal data of multiple types of events where some of the events are observed on a coarser level (e.g. grouped) at some time points during the follow-up, for example, when certain events, such as disease progression, are only observable during parts of follow-up for some subjects, causing gaps in the data, or when the time of death is observed but the cause of death is unknown. In this case, there is missing data in key characteristics of the event history such as onset, time in state, and number of events. We derive the likelihood function, score and observed information under independent and non-informative coarsening, and conduct a simulation study where we compare bias, empirical standard errors, and confidence interval coverage of estimators based on direct maximum likelihood, Monte Carlo Expectation Maximisation, ignoring the coarsening thus acting as if no event occurred, and artificial right censoring at the first time of coarsening. Longitudinal data on drug prescriptions and survival in men receiving palliative treatment for prostate cancer is used to estimate the parameters of one of the data-generating models. We demonstrate that the performance depends on several factors, including sample size and type of coarsening.