Five multivariate Duchenne muscular dystrophy progression models bridging six-minute walk distance and MRI relaxometry of leg muscles

The study aimed to provide quantitative information on the utilization of MRI transverse relaxation time constant (MRI-T2) of leg muscles in DMD clinical trials by developing multivariate disease progression models of Duchenne muscular dystrophy (DMD) using 6-min walk distance (6MWD) and MRI-T2. Clinical data were collected from the prospective and longitudinal ImagingNMD study. Disease progression models were developed by a nonlinear mixed-effect modeling approach. Univariate models of 6MWD and MRI-T2 of five muscles were developed separately. Age at assessment was the time metric. Multivariate models were developed by estimating the correlation of 6MWD and MRI-T2 model variables. Full model estimation approach for covariate analysis and five-fold cross validation were conducted. Simulations were performed to compare the models and predict the covariate effects on the trajectories of 6MWD and MRI-T2. Sigmoid Imax and Emax models best captured the profiles of 6MWD and MRI-T2 over age. Steroid use, baseline 6MWD, and baseline MRI-T2 were significant covariates. The median age at which 6MWD is half of its maximum decrease in the five models was similar, while the median age at which MRI-T2 is half of its maximum increase varied depending on the type of muscle. The models connecting 6MWD and MRI-T2 successfully quantified how individual characteristics alter disease trajectories. The models demonstrate a plausible correlation between 6MWD and MRI-T2, supporting the use of MRI-T2. The developed models will guide drug developers in using the MRI-T2 to most efficient use in DMD clinical trials.

Multivariate modeling of magnetic resonance biomarkers and clinical outcome measures for Duchenne muscular dystrophy clinical trials

Although regulatory agencies encourage inclusion of imaging biomarkers in clinical trials for Duchenne muscular dystrophy (DMD), industry receives minimal guidance on how to use these biomarkers most beneficially in trials. This study aims to identify the optimal use of muscle fat fraction biomarkers in DMD clinical trials through a quantitative disease-drug-trial modeling and simulation approach. We simultaneously developed two multivariate models quantifying the longitudinal associations between 6-minute walk distance (6MWD) and fat fraction measures from vastus lateralis and soleus muscles. We leveraged the longitudinal individual-level data collected for 10 years through the ImagingDMD study. Age of the individuals at assessment was chosen as the time metric. After the longitudinal dynamic of each measure was modeled separately, the selected univariate models were combined using correlation parameters. Covariates, including baseline scores of the measures and steroid use, were assessed using the full model approach. The nonlinear mixed-effects modeling was performed in Monolix. The final models showed reasonable precision of the parameter estimates. Simulation-based diagnostics and fivefold cross-validation further showed the model's adequacy. The multivariate models will guide drug developers on using fat fraction assessment most efficiently using available data, including the widely used 6MWD. The models will provide valuable information about how individual characteristics alter disease trajectories. We will extend the multivariate models to incorporate trial design parameters and hypothetical drug effects to inform better clinical trial designs through simulation, which will facilitate the design of clinical trials that are both more inclusive and more conclusive using fat fraction biomarkers.

Disease progression joint model predicts time to type 1 diabetes onset: Optimizing future type 1 diabetes prevention studies

Clinical trials seeking type 1 diabetes prevention are challenging in terms of identifying patient populations likely to progress to type 1 diabetes within limited (i.e., short-term) trial durations. Hence, we sought to improve such efforts by developing a quantitative disease progression model for type 1 diabetes. Individual-level data obtained from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies were used to develop a joint model that links the longitudinal glycemic measure to the timing of type 1 diabetes diagnosis. Baseline covariates were assessed using a stepwise covariate modeling approach. Our study focused on individuals at risk of developing type 1 diabetes with the presence of two or more diabetes-related autoantibodies (AAbs). The developed model successfully quantified how patient features measured at baseline, including HbA1c and the presence of different AAbs, alter the timing of type 1 diabetes diagnosis with reasonable accuracy and precision (<30% RSE). In addition, selected covariates were statistically significant (p < 0.0001 Wald test). The Weibull model best captured the timing to type 1 diabetes diagnosis. The 2-h oral glucose tolerance values assessed at each visit were included as a time-varying biomarker, which was best quantified using the sigmoid maximum effect function. This model provides a framework to quantitatively predict and simulate the time to type 1 diabetes diagnosis in individuals at risk of developing the disease and thus, aligns with the needs of pharmaceutical companies and scientists seeking to advance therapies aimed at interdicting the disease process.

Development of a model-based clinical trial simulation platform to optimize the design of clinical trials for Duchenne muscular dystrophy

Early clinical trials of therapies to treat Duchenne muscular dystrophy (DMD), a fatal genetic X‐linked pediatric disease, have been designed based on the limited understanding of natural disease progression and variability in clinical measures over different stages of the continuum of the disease. The objective was to inform the design of DMD clinical trials by developing a disease progression model‐based clinical trial simulation (CTS) platform based on measures commonly used in DMD trials. Data were integrated from past studies through the Duchenne Regulatory Science Consortium founded by the Critical Path Institute (15 clinical trials and studies, 1505 subjects, 27,252 observations). Using a nonlinear mixed‐effects modeling approach, longitudinal dynamics of five measures were modeled (NorthStar Ambulatory Assessment, forced vital capacity, and the velocities of the following three timed functional tests: time to stand from supine, time to climb 4 stairs, and 10 meter walk‐run time). The models were validated on external data sets and captured longitudinal changes in the five measures well, including both early disease when function improves as a result of growth and development and the decline in function in later stages. The models can be used in the CTS platform to perform trial simulations to optimize the selection of inclusion/exclusion criteria, selection of measures, and other trial parameters. The data sets and models have been reviewed by the US Food and Drug Administration and the European Medicines Agency; have been accepted into the Fit‐for‐Purpose and Qualification for Novel Methodologies pathways, respectively; and will be submitted for potential endorsement by both agencies.

Machine Learning in Drug Discovery and Development Part 1: A Primer

Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example. A companion article will summarize applications of ML in drug discovery, drug development, and postapproval phase.

Challenges in Alzheimer's Disease Drug Discovery and Development: The Role of Modeling, Simulation, and Open Data

Alzheimer's disease (AD) is the leading cause of dementia worldwide. With 35 million people over 60 years of age with dementia, there is an urgent need to develop new treatments for AD. To streamline this process, it is imperative to apply insights and learnings from past failures to future drug development programs. In the present work, we focus on how modeling and simulation tools can leverage open data to address drug development challenges in AD.

Primary lung cancer in Mexico city: a report of 1019 cases

A retrospective study was performed in patients diagnosed with primary lung cancer, and admitted to the Instituto Nacional de Enfermedades Respiratorias between 1984 to 1992. One thousand and nineteen patients were studied, 636 males and 383 females. We found a higher incidence in the group among 61-70 years of age in both sexes. The highest percentage of tumors were from the adenocarcinoma variety, followed by the epidermoid carcinoma, while the small-cell carcinoma presented a lower incidence. Smoking has been associated with the development of lung cancer. Many of our patients were smokers. The highest frequency was in smokers with a smoking habit of more than 30 years and a rate of more than 10 cigarettes per day. However, an important part of the patients with cancer did not have a smoking habit, which led us to believe that there are other etiological possibility factors (genetic or environmental) that could be involved. Most of the cases of cancer were staged as stage IIIb and stage IV and the patients had an ECOG of 1-2. The main symptoms were coughing, sputum, dyspnea and thoracic pain. This data shows an increasing frequency of lung cancer in Mexico city as well as other countries, it also shows that it is going to be a serious health problem in the future. We consider that in order to improve the prognosis, it is necessary to increase the educational and orientation campaigns among the adult population with or without a smoking history.

Use of a mammalian interspersed repetitive (MIR) element in the coding and processing sequences of mammalian genes

The mammalian interspersed repetitive (MIR) element was amplified in mammals 130 million years ago. The MIR element is at least 260 bp in length and is found in approximately 105 copies in the mammalian genome. We analyzed copies of the MIR element in the DNA of various mammals to determine its relationship to the structure and function of genes, in an attempt to identify specific uses of the MIR element within the mammalian genome. We found that alternative splicing within the acetylcholine receptor gene in humans takes place within the MIR element and results in the incorporation of part of the MIR element into the coding sequence of this gene. Furthermore, the polyadenylation signal (AATAAA) at the 3' end of four different mammalian genes is derived from the MIR element. These uses of the MIR element suggest that other regulatory sequences found within the mammalian genome originated from ancient transposable elements, many of which may no longer be recognizable.

[Evaluation of phagocytosis, bactericidal effect, and cytotoxicity of Pasteurella haemolytica and Pasteurella multocida in bovine alveolar macrophages]

Evaluation of phagocytosis and bactericidal effect of Pasteurella haemolytica and P. multocida was conducted on bovine alveolar macrophages freshly obtained through bronchioalveolar washings from live animals. Cytotoxic activity of these bacteria on the alveolar macrophages was evaluated through the simple visual assay in microplates, using bovine blood leukocytes as a comparative target cell. In order to evaluate phagocytosis the following variables were considered P. haemolytica and P. multocida (independently) in contact with alveolar macrophages, P. haemolytica and P. multocida in suspension as a positive control of bacterial growth, and RPMI-1640 medium alone, as a negative control of bacterial growth. To measure bactericidal capacity, bacteria were incubated with plastic adhered alveolar macrophages at 30 minutes and 3 hours intervals. Samples incubated 30 minutes were taken as phagocytosis-base readings and at the 3 h interval to evaluate bactericidal capacity of the alveolar macrophages on phagocytized bacteria. Reading of the samples of each evaluation was conducted in a spectrophotometer a 380 nm. Phagocytosis results indicated that bacterial proliferation was higher when bacteria were alone as compared when they were with alveolar macrophages (p < 0.05). Bactericidal capacity of the macrophages was efficient because bacterial numbers were higher in the first evaluation as compared to the second (p < 0.05). It was demonstrated that the cytotoxic effect of P. haemolytica was more severe on blood leukocytes as compared to alveolar macrophages (p < 0.05). There was no evidence of P. multocida cytotoxicity on the evaluated cells. With the development of this technique for the obtention of alveolar macrophages and using spectrophotometry for the phagocytosis and bactericidal effect evaluations, numerous variables and samples can be tested, such as opsonized bacteria or to measure the behaviour of alveolar macrophages infected with different agents involved in the bovine pneumonic complex.