Disease Progression Modeling: Key Concepts and Recent Developments

Development and validation of a chronic kidney disease progression model using patient-level simulations

Chronic disease progression models are available for several highly prevalent conditions. For chronic kidney disease (CKD), the scope of existing progression models is limited to the risk of kidney failure and major cardiovascular (CV) events. The aim of this project was to develop a comprehensive CKD progression model (CKD-PM) that simulates the risk of CKD progression and a broad range of complications in patients with CKD. A series of literature reviews informed the selection of risk factors and identified existing risk equations/algorithms for kidney replacement therapy (KRT), CV events, other CKD-related complications, and mortality. Risk equations and transition probabilities were primarily sourced from publications produced by large US and international CKD registries. A patient-level, state-transition model was developed with health states defined by the Kidney Disease Improving Global Outcomes categories. Model validation was performed by comparing predicted outcomes with observed outcomes in the source cohorts used in model development (internal validation) and other cohorts (external validation). The CKD-PM demonstrated satisfactory modeling properties. Accurate prediction of all-cause and CV mortality was achieved without calibration, while prediction of CV events through CKD-specific equations required implementation of a calibration factor to balance time-dependent versus baseline risk. Predicted annual changes in estimated glomerular filtration rate (eGFR) and urine albumin-creatinine ratio were acceptable in comparison to external values. A flexible eGFR threshold for KRT equations enabled accurate prediction of these events. This CKD-PM demonstrated reliable modeling properties. Both internal and external validation revealed robust outcomes.

Clinical outcome-guided deep temporal clustering for disease progression subtyping

OBJECTIVE

Complex diseases exhibit heterogeneous progression patterns, necessitating effective capture and clustering of longitudinal changes to identify disease subtypes for personalized treatments. However, existing studies often fail to design clustering-specific representations or neglect clinical outcomes, thereby limiting the interpretability and clinical utility.

METHOD

We design a unified framework for subtyping longitudinal progressive diseases. We focus on effectively integrating all data from disease progressions and improving patient representation for downstream clustering. Specifically, we propose a clinical Outcome-Guided Deep Temporal Clustering (OG-DTC) that generates representations informed by clustering and clinical outcomes. A GRU-based seq2seq architecture captures the temporal dynamics, and the model integrates k-means clustering and outcome regression to facilitate the formation of clustering structures and the integration of clinical outcomes. The learned representations are clustered using a Gaussian mixture model to identify distinct subtypes. The clustering results are extensively validated through reproducibility, stability, and significance tests.

RESULTS

We demonstrated the efficacy of our framework by applying it to three Alzheimer's Disease (AD) clinical trials. Through the AD case study, we identified three distinct subtypes with unique patterns associated with differentiated clinical declines across multiple measures. The ablation study revealed the contributions of each component in the model and showed that jointly optimizing the full model improved patient representations for clustering. Extensive validations showed that the derived clustering is reproducible, stable, and significant.

CONCLUSION

Our temporal clustering framework can derive robust clustering applicable for subtyping longitudinal progressive diseases and has the potential to account for subtype variability in clinical outcomes.

Development of a Cohort Analytics Tool for Monitoring Progression Patterns in Cardiovascular Diseases: Advanced Stochastic Modeling Approach

BACKGROUND

The World Health Organization (WHO) reported that cardiovascular diseases (CVDs) are the leading cause of death worldwide. CVDs are chronic, with complex progression patterns involving episodes of comorbidities and multimorbidities. When dealing with chronic diseases, physicians often adopt a "watchful waiting" strategy, and actions are postponed until information is available. Population-level transition probabilities and progression patterns can be revealed by applying time-variant stochastic modeling methods to longitudinal patient data from cohort studies. Inputs from CVD practitioners indicate that tools to generate and visualize cohort transition patterns have many impactful clinical applications. The resultant computational model can be embedded in digital decision support tools for clinicians. However, to date, no study has attempted to accomplish this for CVDs.

OBJECTIVE

This study aims to apply advanced stochastic modeling methods to uncover the transition probabilities and progression patterns from longitudinal episodic data of patient cohorts with CVD and thereafter use the computational model to build a digital clinical cohort analytics artifact demonstrating the actionability of such models.

METHODS

Our data were sourced from 9 epidemiological cohort studies by the National Heart Lung and Blood Institute and comprised chronological records of 1274 patients associated with 4839 CVD episodes across 16 years. We then used the continuous-time Markov chain method to develop our model, which offers a robust approach to time-variant transitions between disease states in chronic diseases.

RESULTS

Our study presents time-variant transition probabilities of CVD state changes, revealing patterns of CVD progression against time. We found that the transition from myocardial infarction (MI) to stroke has the fastest transition rate (mean transition time 3, SD 0 days, because only 1 patient had a MI-to-stroke transition in the dataset), and the transition from MI to angina is the slowest (mean transition time 1457, SD 1449 days). Congestive heart failure is the most probable first episode (371/840, 44.2%), followed by stroke (216/840, 25.7%). The resultant artifact is actionable as it can act as an eHealth cohort analytics tool, helping physicians gain insights into treatment and intervention strategies. Through expert panel interviews and surveys, we found 9 application use cases of our model.

CONCLUSIONS

Past research does not provide actionable cohort-level decision support tools based on a comprehensive, 10-state, continuous-time Markov chain model to unveil complex CVD progression patterns from real-world patient data and support clinical decision-making. This paper aims to address this crucial limitation. Our stochastic model-embedded artifact can help clinicians in efficient disease monitoring and intervention decisions, guided by objective data-driven insights from real patient data. Furthermore, the proposed model can unveil progression patterns of any chronic disease of interest by inputting only 3 data elements: a synthetic patient identifier, episode name, and episode time in days from a baseline date.

Population Pharmacokinetic-Pharmacodynamic Analysis of a Reserpine-Induced Myalgia Model in Rats

(1) Background: Fibromyalgia syndrome (FMS) is a chronic pain condition with widespread pain and multiple comorbidities, for which conventional therapies offer limited benefits. The reserpine-induced myalgia (RIM) model is an efficient animal model of FMS in rodents. This study aimed to develop a pharmacokinetic-pharmacodynamic (PK-PD) model of reserpine in rats, linking to its impact on monoamines (MAs). (2) Methods: Reserpine was administered daily for three consecutive days at dose levels of 0.1, 0.5, and 1 mg/kg. A total of 120 rats were included, and 120 PK and 828 PD observations were collected from 48 to 96 h after the first dose of reserpine. Non-linear mixed-effect data analysis was applied for structural PK-PD model definition, variability characterization, and covariate analysis. (3) Results: A one-compartment model best described reserpine in rats (V = 1.3 mL/kg and CL = 4.5 × 10-1 mL/h/kg). A precursor-pool PK-PD model (kin = 6.1 × 10-3 mg/h, kp = 8.6 × 10-4 h-1 and kout = 2.7 × 10-2 h-1) with a parallel transit chain (k0 = 1.9 × 10-1 h-1) characterized the longitudinal levels of MA in the prefrontal cortex, spinal cord, and amygdala in rats. Reserpine stimulates the degradation of MA from the pool compartment (Slope1 = 1.1 × 10-1 h) and the elimination of MA (Slope2 = 1.25 h) through the transit chain. Regarding the reference dose (1 mg/kg) of the RIM model, the administration of 4 mg/kg would lead to a mean reduction of 65% (Cmax), 80% (Cmin), and 70% (AUC) of MA across the brain regions tested. (4) Conclusions: Regional brain variations in neurotransmitter depletion were identified, particularly in the amygdala, offering insights for therapeutic strategies and biomarker identification in FMS research.

Prediction of Disease Progression and Clinical Response in Systemic Sclerosis: Experience From a Proof-of-Concept Trial

OBJECTIVE

Using the modified Rodnan skin score (mRSS) as a surrogate for disease activity, a phase 2a study in patients with systemic sclerosis (SSc) measured efficacy of the autotaxin inhibitor ziritaxestat. Mathematical modeling of mRSS was used to predict disease progression, examine candidate trial designs, and predict the probability of successfully discriminating treatment effect.

METHODS

Patients with SSc receiving 600 mg of ziritaxestat or placebo for 24 weeks were included, in addition to data up to week 52 of the open-label extension (OLE). Longitudinal mRSS data were described using a disease progression model; drug effect was a binary variable. Parameters used to predict the OLE mRSS outcome were estimated using data from the 24-week double-blind phase and validated with observed data. Three trial designs were simulated to identify which had the highest probability of detecting a treatment effect. Power to detect a treatment effect was quantified using the simulations.

RESULTS

Maximum decreases from baseline in mRSS were 50.4% (ziritaxestat) and 34.7% (placebo). Study designs based on 300 patients randomized 2:1 or 1:1 to 600 mg of ziritaxestat or placebo had similar probabilities of detecting a significant treatment effect. Power to detect a treatment effect was >80% for all simulations.

CONCLUSION

Disease progression and drug effect could be predicted beyond the range of observed data. This modeling and simulation approach may inform future trial design, including study duration, and predict the probability of success.

Disease progression modeling with temporal realignment: An emerging approach to deepen knowledge on chronic diseases

The recent development of the first disease-modifying drug for Alzheimer's disease represents a major advancement in dementia treatment. Behind this breakthrough is a quarter century of research efforts to understand the disease not by a particular symptom at a given moment, but by long-term sequential changes in multiple biomarkers. Disease progression modeling with temporal realignment (DPM-TR) is an emerging computational approach proposed with this biomarker-based disease concept. By integrating short-term clinical observations of multiple disease biomarkers in a data-driven manner, DPM-TR provides a way to understand the progression of chronic diseases over decades and predict individual disease stages more accurately. DPM-TR has been developed primarily in the area of neurodegenerative diseases but has recently been extended to non-neurodegenerative diseases, including chronic obstructive pulmonary, autoimmune, and ophthalmologic diseases. This review focuses on opportunities for DPM-TR in clinical practice and drug development and discusses its current status and challenges.

Factors associated with pathologic myopia onset and progression: A systematic review and meta-analysis

PURPOSE

To synthesise evidence across studies on factors associated with pathologic myopia (PM) onset and progression based on the META-analysis for Pathologic Myopia (META-PM) classification framework.

METHODS

Findings from six longitudinal studies (5-18 years) were narratively synthesised and meta-analysed, using odds ratio (OR) as the common measure of association. All studies adjusted for baseline myopia, age and sex at a minimum. The quality of evidence was rated using the Grades of Recommendation, Assessment, Development and Evaluation framework.

RESULTS

Five out of six studies were conducted in Asia. There was inconclusive evidence of an independent effect (or lack thereof) of ethnicity and sex on PM onset/progression. The odds of PM onset increased with greater axial length (pooled OR: 2.03; 95% CI: 1.71-2.40; p < 0.001), older age (pooled OR: 1.07; 1.05-1.09; p < 0.001) and more negative spherical equivalent refraction, SER (OR: 0.77; 0.68-0.87; p < 0.001), all of which were supported by an acceptable level of evidence. Fundus tessellation was found to independently increase the odds of PM onset in a population-based study (OR: 3.02; 2.58-3.53; p < 0.001), although this was only supported by weak evidence. There was acceptable evidence that greater axial length (pooled OR: 1.23; 1.09-1.39; p < 0.001), more negative SER (pooled OR: 0.87; 0.83-0.92; p < 0.001) and higher education level (pooled OR: 3.17; 1.36-7.35; p < 0.01) increased the odds of PM progression. Other baseline factors found to be associated with PM progression but currently supported by weak evidence included age (pooled OR: 1.01), severity of myopic maculopathy (OR: 3.61), intraocular pressure (OR: 1.62) and hypertension (OR: 0.21).

CONCLUSIONS

Most PM risk/prognostic factors are not supported by an adequate evidence base at present (an indication that PM remains understudied). Current factors for which an acceptable level of evidence exists (limited in number) are unmodifiable in adults and lack personalised information. More longitudinal studies focusing on uncovering modifiable factors and imaging biomarkers are warranted.

Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development

Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.

Data-driven disease progression model of Parkinson's disease and effect of sex and genetic variants

As Parkinson's disease (PD) progresses, there are multiple biomarker changes, and sex and genetic variants may influence the rate of progression. Data-driven, long-term disease progression model analysis may provide precise knowledge of the relationships between these risk factors and progression and would allow for the selection of appropriate diagnosis and treatment according to disease progression. To construct a long-term disease progression model of PD based on multiple biomarkers and evaluate the effects of sex and leucine-rich repeat kinase 2 (LRRK2) mutations, a technique derived from the nonlinear mixed-effects model (Statistical Restoration of Fragmented Time course [SReFT]) was applied to datasets of patients provided by the Parkinson's Progression Markers Initiative. Four biomarkers, including the Unified PD Rating Scale, were used, and a covariate analysis was performed to investigate the effects of sex and LRRK2-related mutations. A model of disease progression over ~30 years was successfully developed using patient data with a median of 6 years. Covariate analysis suggested that female sex and LRRK2 G2019S mutations were associated with 21.6% and 25.4% significantly slower progression, respectively. LRRK2 rs76904798 mutation also tended to delay disease progression by 10.4% but the difference was not significant. In conclusion, a long-term PD progression model was successfully constructed using SReFT from relatively short-term individual patient observations and depicted nonlinear changes in relevant biomarkers and their covariates, including sex and genetic variants.

Transitioning from Subtyping to Precision Medicine in Parkinson's Disease: A Purpose-Driven Approach

The International Parkinson and Movement Disorder Society (MDS) created a task force (TF) to provide a critical overview of the Parkinson's disease (PD) subtyping field and develop a guidance on future research in PD subtypes. Based on a literature review, we previously concluded that PD subtyping requires an ultimate alignment with principles of precision medicine, and consequently novel approaches were needed to describe heterogeneity at the individual patient level. In this manuscript, we present a novel purpose-driven framework for subtype research as a guidance to clinicians and researchers when proposing to develop, evaluate, or use PD subtypes. Using a formal consensus methodology, we determined that the key purposes of PD subtyping are: (1) to predict disease progression, for both the development of therapies (use in clinical trials) and prognosis counseling, (2) to predict response to treatments, and (3) to identify therapeutic targets for disease modification. For each purpose, we describe the desired product and the research required for its development. Given the current state of knowledge and data resources, we see purpose-driven subtyping as a pragmatic and necessary step on the way to precision medicine. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Surrogacy and the Valuation of ATMPs: Taking Our Place in the Evidence Generation/Assessment Continuum

Medical technology is advancing rapidly, but established methods for health technology assessment are struggling to keep up. This challenge is particularly stark for the assessment of advanced therapy medicinal products-therapies often launched on the basis of single-arm studies powered to a surrogate primary endpoint. The most robust surrogacy methods investigate trial-level correlations between the treatment effect on the surrogate and the outcome of ultimate interest. However, these methods are often impossible with the evidence usually available for advanced therapy medicinal products at the time of the launch (randomized controlled trials are necessary for these advanced methods). Additionally, these surrogacy relationships are usually considered to be technology specific, adding uncertainty for any approach that primarily relies on historic data to estimate the surrogacy relationship for novel interventions such as advanced therapy medicinal products. The literature has already highlighted the need for early dialogue, staged assessment processes, and pricing arrangements that responsibly share the risk between the manufacturer and payer. However, it is our view that in addition to these critical developments, the modeling methods employed could also improve. Currently, health technology assessment practitioners typically either ignore the surrogate and simply extrapolate the endpoint of greatest patient relevance irrespective of the degree of maturity or assume historic surrogate relationships apply to the novel technology. In this opinion piece, we outline an additional avenue. By drawing on the understanding of the mechanism of action and insights generated earlier in the evidence generation/assessment continuum, cost-effectiveness modelers can make better use of the wider data available. These efforts are expected to reduce uncertainty at the time of the initial launch of pharmaceutical products and increase the value of subsequent data collection efforts.

Agent-based modeling in medical research, virtual baseline generator and change in patients' profile issue

Simulation studies are promising in medical research in particular to improve drug development. For instance, one can aim to develop In Silico Clinical Trial in order to challenge trial's design parameters in terms of feasibility and probability of success of the trial. Approaches based on agent-based models draw on a particularly useful framework to simulate patients evolution. In this paper, an approach based on agent-based modeling is described and discussed in the context of medical research. An R-vine copula model is used to represent the multivariate distribution of the data. A baseline data cohort can then be simulated and execution models can be developed to simulate the evolution of patients. R-vine copula models are very flexible tools which allow researchers to consider different marginal distributions than the ones observed in the data. It is then possible to perform data augmentation to explore a new population by simulating baseline data which are slightly different than those of the original population. A simulation study illustrates the efficiency of copula modeling to generate data according to specific marginal distributions but also highlights difficulties inherent to data augmentation.

Longitudinal Meta-Analysis of Historical Parkinson's Disease Trials to Inform Future Trial Design

BACKGROUND

The outcome of clinical trials in neurodegeneration can be highly uncertain due to the presence of a strong placebo effect.

OBJECTIVES

To develop a longitudinal model that can enhance the success of future Parkinson's disease trials by quantifying trial-to-trial variations in placebo and active treatment response.

METHODS

A longitudinal model-based meta-analysis was conducted on the total score of Unified Parkinson's Disease Rating Scale (UPDRS) Parts 1, 2, and 3. The analysis included aggregate data from 66 arms (observational [4], placebo [28], or investigational-drug-treated [34]) from 4 observational studies and 17 interventional trials. Inter-study variabilities in key parameters were estimated. Residual variability was weighted by the size of study arms.

RESULTS

The baseline total UPDRS was estimated to average at 24.5 points. Disease score was estimated to worsen by 3.90 points/year for the duration of the treatments; whilst notably, arms with a lower baseline progressed faster. The model captured the transient nature of the placebo response and sustained symptomatic drug effect. Both placebo and drug effects peaked within 2 months; although, 1 year was needed to observe the full treatment difference. Across these studies, the progression rate varied by 59.4%, the half-life for offset of placebo response varied by 79.4%, and the amplitude for drug effect varied by 105.3%.

CONCLUSION

The longitudinal model-based meta-analysis describes UPDRS progression rate, captures the dynamics of the placebo response, quantifies the effect size of the available therapies, and sets the expectation of uncertainty for future trials. The findings provide informative priors to enhance the rigor and success of future trials of promising agents, including potential disease modifiers. © 2023 GSK. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development

Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.

Opportunities and Challenges of Disease Progression Modeling in Drug Development - An IQ Perspective

Disease progression modeling (DPM) represents an important model-informed drug development framework. The scientific communities support the use of DPM to accelerate and increase efficiency in drug development. This article summarizes International Consortium for Innovation & Quality (IQ) in Pharmaceutical Development mediated survey conducted across multiple biopharmaceutical companies on challenges and opportunities for DPM. Additionally, this summary highlights the viewpoints of IQ from the 2021 workshop hosted by the US Food and Drug Administration (FDA). Sixteen pharmaceutical companies participated in the IQ survey with 36 main questions. The types of questions included single/multiple choice, dichotomous, rank questions, and open-ended or free text. The key results show that DPM has different representation, it encompasses natural disease history, placebo response, standard of care as background therapy, and can even be interpreted as pharmacokinetic/pharmacodynamic modeling. The most common reasons for not implementing DPM as frequently seem to be difficulties in internal cross-functional alignment, lack of knowledge of disease/data, and time constraints. If successfully implemented, DPM can have an impact on dose selection, reduction of sample size, trial read-out support, patient selection/stratification, and supportive evidence for regulatory interactions. The key success factors and key challenges of disease progression models were highlighted in the survey and about 24 case studies across different therapeutic areas were submitted from various survey sponsors. Although DPM is still evolving, its current impact is limited but promising. The success of such models in the future will depend on collaboration, advanced analytics, availability of and access to relevant and adequate-quality data, collaborative regulatory guidance, and published examples of impact.

Landscape analysis for a neonatal disease progression model of bronchopulmonary dysplasia: Leveraging clinical trial experience and real-world data

The 21^st Century Cures Act requires FDA to expand its use of real-world evidence (RWE) to support approval of previously approved drugs for new disease indications and post-marketing study requirements. To address this need in neonates, the FDA and the Critical Path Institute (C-Path) established the International Neonatal Consortium (INC) to advance regulatory science and expedite neonatal drug development. FDA recently provided funding for INC to generate RWE to support regulatory decision making in neonatal drug development. One study is focused on developing a validated definition of bronchopulmonary dysplasia (BPD) in neonates. BPD is difficult to diagnose with diverse disease trajectories and few viable treatment options. Despite intense research efforts, limited understanding of the underlying disease pathobiology and disease projection continues in the context of a computable phenotype. It will be important to determine if: 1) a large, multisource aggregation of real-world data (RWD) will allow identification of validated risk factors and surrogate endpoints for BPD, and 2) the inclusion of these simulations will identify risk factors and surrogate endpoints for studies to prevent or treat BPD and its related long-term complications. The overall goal is to develop qualified, fit-for-purpose disease progression models which facilitate credible trial simulations while quantitatively capturing mechanistic relationships relevant for disease progression and the development of future treatments. The extent to which neonatal RWD can inform these models is unknown and its appropriateness cannot be guaranteed. A component of this approach is the critical evaluation of the various RWD sources for context-of use (COU)-driven models. The present manuscript defines a landscape of the data including targeted literature searches and solicitation of neonatal RWD sources from international stakeholders; analysis plans to develop a family of models of BPD in neonates, leveraging previous clinical trial experience and real-world patient data is also described.

Role of Disease Progression Models in Drug Development

The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease knowledge to elucidate the impact of novel therapeutics or vaccines on disease course, thus quantifying the potential clinical benefit at different stages of drug development programs. This paper provides a brief overview of DPMs and the evolution in data types, analytic methods, and applications that have occurred in their use by Quantitive Clinical Pharmacologists. It also provides examples of how these models have informed decisions and clinical trial design across several therapeutic areas and at various stages of development. It briefly describes potential new applications of DPMs utilizing emerging data sources, and utilizing new analytic techniques, and discuss new challenges faced such as requiring description of multiple endpoints, rapid model development, application of machine learning-based analytics, and use of high dimensional and real-world data. Considerations for the continued evolution future of DPMs to serve as community-maintained expert systems are also provided.

A Fair and Safe Usage Drug Recommendation System in Medical Emergencies by a Stacked ANN

The importance of online recommender systems for drugs, medical professionals, and hospitals is growing. Today, the majority of people use online consultations for drug recommendations for all types of health issues. Emergencies such as pandemics, floods, or cyclones can be helped by the medical recommender system. In the era of machine learning (ML), recommender systems produce more accurate, quick, and reliable clinical predictions with minimal costs. As a result, these systems maintain better performance, integrity, and privacy of patient data in the decision-making process and provide precise information at any time. Therefore, we present drug recommender systems with a stacked artificial neural network (ANN) model to improve the fairness and safety of treatment for infectious diseases. To reduce side effects, drugs are recommended based on a patient’s previous health profile, lifestyle, and habits. The proposed system produced results with 97.5% accuracy. A system such as this could be useful in recommending safe medicines to patients, especially during health emergencies.

Pharmacokinetic/Pharmacodynamic Evaluation of a New Purine-2,6-Dione Derivative in Rodents with Experimental Autoimmune Diseases

Current treatment strategies of autoimmune diseases (ADs) display a limited efficacy and cause numerous adverse effects. Phosphodiesterase (PDE)4 and PDE7 inhibitors have been studied recently as a potential treatment of a variety of ADs. In this study, a PK/PD disease progression modeling approach was employed to evaluate effects of a new theophylline derivative, compound 34, being a strong PDE4 and PDE7 inhibitor. Activity of the studied compound against PDE1 and PDE3 in vitro was investigated. Animal models of multiple sclerosis (MS), rheumatoid arthritis (RA), and autoimmune hepatitis were utilized to assess the efficacy of this compound, and its pharmacokinetics was investigated in mice and rats. A new PK/PD disease progression model of compound 34 was developed that satisfactorily predicted the clinical score-time courses in mice with experimental encephalomyelitis that is an animal model of MS. Compound 34 displayed a high efficacy in all three animal models of ADs. Simultaneous inhibition of PDE types located in immune cells may constitute an alternative treatment strategy of ADs. The PK/PD encephalomyelitis and arthritis progression models presented in this study may be used in future preclinical research, and, upon modifications, may enable translation of the results of preclinical investigations into the clinical settings.

A hierarchical Bayesian entry time realignment method to study the long-term natural history of diseases

A major question in clinical science is how to study the natural course of a chronic disease from inception to end, which is challenging because it is impractical to follow patients over decades. Here, we developed BETR (Bayesian entry time realignment), a hierarchical Bayesian method for investigating the long-term natural history of diseases using data from patients followed over short durations. A simulation study shows that BETR outperforms an existing method that ignores patient-level variation in progression rates. BETR, when combined with a common Bayesian model comparison tool, can identify the correct disease progression function nearly 100% of the time, with high accuracy in estimating the individual disease durations and progression rates. Application of BETR in patients with geographic atrophy, a disease with a known natural history model, shows that it can identify the correct disease progression model. Applying BETR in patients with Huntington's disease demonstrates that the progression of motor symptoms follows a second order function over approximately 20 years.

Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?

Health technology assessment (HTA) aims to be a systematic, transparent, unbiased synthesis of clinical efficacy, safety, and value of medical products (MPs) to help policymakers, payers, clinicians, and industry to make informed decisions. The evidence available for HTA has gaps-impeding timely prediction of the individual long-term effect in real clinical practice. Also, appraisal of an MP needs cross-stakeholder communication and engagement. Both aspects may benefit from extended use of modeling and simulation. Modeling is used in HTA for data-synthesis and health-economic projections. In parallel, regulatory consideration of model informed drug development (MIDD) has brought attention to mechanistic modeling techniques that could in fact be relevant for HTA. The ability to extrapolate and generate personalized predictions renders the mechanistic MIDD approaches suitable to support translation between clinical trial data into real-world evidence. In this perspective, we therefore discuss concrete examples of how mechanistic models could address HTA-related questions. We shed light on different stakeholder's contributions and needs in the appraisal phase and suggest how mechanistic modeling strategies and reporting can contribute to this effort. There are still barriers dissecting the HTA space and the clinical development space with regard to modeling: lack of an adapted model validation framework for decision-making process, inconsistent and unclear support by stakeholders, limited generalizable use cases, and absence of appropriate incentives. To address this challenge, we suggest to intensify the collaboration between competent authorities, drug developers and modelers with the aim to implement mechanistic models central in the evidence generation, synthesis, and appraisal of HTA so that the totality of mechanistic and clinical evidence can be leveraged by all relevant stakeholders.

Influence of Subject-Specific Effects in Longitudinal Modelling of Cognitive Decline in Alzheimer's Disease

BACKGROUND

Accurate longitudinal modelling of cognitive decline is a major goal of Alzheimer's disease and related dementia (ADRD) research. However, the impact of subject-specific effects is not well characterized and may have implications for data generation and prediction.

OBJECTIVE

This study seeks to address the impact of subject-specific effects, which are a less well-characterized aspect of ADRD cognitive decline, as measured by the Alzheimer's Disease Assessment Scale's Cognitive Subscale (ADAS-Cog).

METHODS

Prediction errors and biases for the ADAS-Cog subscale were evaluated when using only population-level effects, robust imputation of subject-specific effects using model covariances, and directly known individual-level effects fit during modelling as a natural control. Evaluated models included pre-specified parameterizations for clinical trial simulation, analogous mixed-effects regression models parameterized directly, and random forest ensemble models. Assessment used a meta-database of Alzheimer's disease studies with validation in simulated synthetic cohorts.

RESULTS

All models observed increases in variance under imputation leading to increased prediction error. Bias decreased with imputation except under the pre-specified parameterization, which increased in the meta-database, but was attenuated under simulation. Known fitted subject effects gave the best prediction results.

CONCLUSION

Subject-specific effects were found to have a profound impact on predicting ADAS-Cog. Reductions in bias suggest imputing random effects assists in calculating results on average, as when simulating clinical trials. However, reduction in error emphasizes population-level effects when attempting to predict outcomes for individuals. Forecasting future observations greatly benefits from using known subject-specific effects.

Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features

In Alzheimer's disease (AD) progression, it is imperative to identify the subjects with mild cognitive impairment before clinical symptoms of AD appear. This work proposes a technique for decision support in identifying subjects who will show transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD) in the future. We used robust predictors from multivariate MRI-derived biomarkers and neuropsychological measures and tracked their longitudinal trajectories to predict signs of AD in the MCI population. Assuming piecewise linear progression of the disease, we designed a novel weighted gradient offset-based technique to forecast the future marker value using readings from at least two previous follow-up visits. Later, the complete predictor trajectories are used as features for a standard support vector machine classifier to identify MCI-to-AD progressors amongst the MCI patients enrolled in the Alzheimer's disease neuroimaging initiative (ADNI) cohort. We explored the performance of both unimodal and multimodal models in a 5-fold cross-validation setup. The proposed technique resulted in a high classification AUC of 91.2% and 95.7% for 6-month- and 1-year-ahead AD prediction, respectively, using multimodal markers. In the end, we discuss the efficacy of MRI markers as compared to NM for MCI-to-AD conversion prediction.

DPVis: Visual Analytics With Hidden Markov Models for Disease Progression Pathways

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this article, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.

Towards Predictive Analysis on Disease Progression: A Variational Hawkes Process Model

Massively available longitudinal data about long-term disease trajectories of patients provides a golden mine for the understanding of disease progression and efficient health service delivery. It calls for quantitative modeling of disease progression, which is a tricky problem due to the complexity of the disease progression process as well as the irregularity of time documented in trajectories. In this study, we tackle the problem with the goal of predictively analyzing disease progression. Specifically, we propose a novel Variational Hawkes Process (VHP) model to generalize disease progression and predict future patient states based on the clinical observational data of past disease trajectories. First, Hawkes Process captures the intensity of irregular visits in a trajectory documented on medical facilities and controls the aforementioned information flowing into future visits. Thereafter, the captured intensity is incorporated into a Variational Auto-Encoder to generate the representation of the future partial disease trajectory for a target patient in a predictive manner. To further improve the prediction performance, we equip the proposed model with a disease trajectory discriminator to distinguish the generated trajectories from real ones. We evaluate the proposed model on two public datasets from the MIMIC-III database pertaining to heart failure and sepsis patients, respectively, and one real-world dataset from a Chinese hospital pertaining to heart failure patients with multiple admissions. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art baselines, and may derive a set of practical implications that can benefit a wide spectrum of management and applications on disease progression.

Pharmacometrics meets statistics-A synergy for modern drug development

Modern drug development problems are very complex and require integration of various scientific fields. Traditionally, statistical methods have been the primary tool for design and analysis of clinical trials. Increasingly, pharmacometric approaches using physiology-based drug and disease models are applied in this context. In this paper, we show that statistics and pharmacometrics have more in common than what keeps them apart, and collectively, the synergy from these two quantitative disciplines can provide greater advances in clinical research and development, resulting in novel and more effective medicines to patients with medical need.

Model-based meta-analysis of the time to first acute urinary retention or benign prostatic hyperplasia-related surgery in patients with moderate or severe symptoms

AIMS

Combination therapy of 5α-reductase inhibitor and α-blocker is a guideline-endorsed therapeutic approach for patients with moderate-to-severe lower urinary tract symptoms or benign prostatic hyperplasia (LUTS/BPH) who are at risk of disease progression. We aimed to disentangle the contribution of clinical and demographic baseline characteristics affecting the risk of acute urinary retention or BPH-related surgery (AUR/S) from the effect of treatment with drugs showing symptomatic and disease-modifying properties.

METHODS

A time-to-event model was developed using pooled data from patients (n = 10 238) enrolled into six clinical studies receiving placebo, tamsulosin, dutasteride or tamsulosin-dutasteride combination therapy. A parametric hazard function was used to describe the time to first AUR/S. Covariate model building included the assessment of relevant clinical and demographic factors on baseline hazard. Predictive performance was evaluated by graphical and statistical methods.

RESULTS

An exponential hazard model best described the time to first AUR/S in this group of patients. Baseline International Prostate Symptom Score, prostate-specific antigen, prostate volume and maximum urine flow were identified as covariates with hazard ratio estimates of 1.04, 1.08, 1.01 and 0.91, respectively. Dutasteride monotherapy and tamsulosin-dutasteride combination therapy resulted in a significant reduction in the baseline hazard (56.8% and 66.4%, respectively). By contrast, the effect of tamsulosin did not differ from placebo.

CONCLUSIONS

Our analysis showed the implications of disease-modifying properties of dutasteride and tamsulosin-dutasteride combination therapy for the risk of AUR/S. It also elucidated the contribution of different baseline characteristics to the risk of these events. The use of tamsulosin monotherapy (symptomatic treatment) has no impact on individual long-term risk.

Exposure-efficacy analyses of nintedanib in patients with chronic fibrosing interstitial lung disease

BACKGROUND

The tyrosine kinase inhibitor nintedanib reduces the rate of decline in forced vital capacity (FVC) in patients with idiopathic pulmonary fibrosis (IPF), other chronic fibrosing interstitial lung diseases (ILDs) with a progressive phenotype and systemic sclerosis-associated ILD (SSc-ILD). The recommended dose of nintedanib is 150 mg twice daily (BID).

METHODS

Data from Phase II and III trials in IPF, SSc-ILD and progressive fibrosing ILDs other than IPF were analyzed to investigate the relationship between nintedanib plasma concentrations (exposure) and efficacy.

RESULTS

Using data from 1403 patients with IPF treated with 50-150 mg nintedanib BID in Phase II and III studies, a linear disease progression model with a maximum drug effect on the rate of decline in FVC was established. Age, height and gender were pre-specified covariates on baseline FVC. Stepwise analysis revealed no other covariates with a distinct effect on the exposure-efficacy relationship. The estimated plasma concentration producing 80% of the maximum drug effect was 10-13 ng/mL, close to the median exposure at 150 mg BID (10 ng/mL). The model in IPF was adapted using Phase III data from 575 patients with SSc-ILD and 663 patients with progressive fibrosing ILDs other than IPF. Besides differences in the natural decline in FVC without treatment, data were consistent with the exposure-efficacy relationship in IPF.

CONCLUSIONS

For most patients with chronic fibrosing ILDs, the 150 mg nintedanib BID dose provides exposure levels associated with a therapeutic effect close to the maximum nintedanib effect independent of disease condition or baseline demographics.

cLRT-Mod: An efficient methodology for pharmacometric model-based analysis of longitudinal phase II dose finding studies under model uncertainty

Within the challenging context of phase II dose-finding trials, longitudinal analyses may increase drug effect detection power compared to an end-of-treatment analysis. This work proposes cLRT-Mod, a pharmacometric adaptation of the MCP-Mod methodology, which allows the use of nonlinear mixed effect models to first detect a dose-response signal and then identify the doses for the confirmatory phase while accounting for model structure uncertainty. The method was evaluated through extensive clinical trial simulations of a hypothetical phase II dose-finding trial using different scenarios and comparing different methods such as MCP-Mod. The results show an increase in power using cLRT with longitudinal data compared to an EOT multiple contrast tests for scenarios with small sample size and weak drug effect while maintaining pre-specifiability of the models prior to data analysis and the nominal type I error. This work shows how model averaging provides better coverage probability of the drug effect in the prediction step, and avoids under-estimation of the size of the confidence interval. Finally, for illustration purpose cLRT-Mod was applied to the analysis of a real phase II dose-finding trial.

Modeling Disease Progression Trajectories from Longitudinal Observational Data

Analyzing disease progression patterns can provide useful insights into the disease processes of many chronic conditions. These analyses may help inform recruitment for prevention trials or the development and personalization of treatments for those affected. We learn disease progression patterns using Hidden Markov Models (HMM) and distill them into distinct trajectories using visualization methods. We apply it to the domain of Type 1 Diabetes (T1D) using large longitudinal observational data from the T1DI study group. Our method discovers distinct disease progression trajectories that corroborate with recently published findings. In this paper, we describe the iterative process of developing the model. These methods may also be applied to other chronic conditions that evolve over time.

Impact of early vs. delayed initiation of dutasteride/tamsulosin combination therapy on the risk of acute urinary retention or BPH-related surgery in LUTS/BPH patients with moderate-to-severe symptoms at risk of disease progression

Purpose

To evaluate the effect of delayed start of combination therapy (CT) with dutasteride 0.5 mg and tamsulosin 0.4 mg on the risk of acute urinary retention or benign prostatic hyperplasia (BPH)-related surgery (AUR/S) in patients with moderate-to-severe lower urinary tract symptoms (LUTS) at risk of disease progression.

Methods

Using a time-to-event model based on pooled data from 10,238 patients from Phase III/IV dutasteride trials, clinical trial simulations (CTS) were performed to assess the risk of AUR/S up to 48 months in moderate-to-severe LUTS/BPH patients following immediate and delayed start of CT for those not responding to tamsulosin monotherapy. Simulation scenarios (1300 subjects/arm) were investigated, including immediate start (reference) and alternative delayed start (six scenarios 1–24 months). AUR/S incidence was described by Kaplan–Meier survival curves and analysed using log-rank test. The cumulative incidence of events as well as the relative and attributable risks were summarised stratified by treatment.

Results

Survival curves for patients starting CT at month 1 and 3 did not differ from those who initiated CT immediately. By contrast, significant differences (p < 0.001) were observed when switch to CT occurs ≥ 6 months from the initial treatment. At month 48, AUR/S incidence was 4.6% vs 9.5%, 11.0% and 11.3% in patients receiving immediate CT vs. switchers after 6, 12 and 24 months, respectively.

Conclusions

Start of CT before month 6 appears to significantly reduce the risk of AUR/S compared with delayed start by ≥ 6 months. This has implications for the treatment algorithm for men with LUTS/BPH at risk of disease progression.

Electronic supplementary material

The online version of this article (10.1007/s00345-020-03517-0) contains supplementary material, which is available to authorized users.

Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms

It is widely believed that cooperation between clinicians and machines may address many of the decisional fragilities intrinsic to current medical practice. However, the realization of this potential will require more precise definitions of disease states as well as their dynamics and interactions. A careful probabilistic examination of symptoms and signs, including the molecular profiles of the relevant biochemical networks, will often be required for building an unbiased and efficient diagnostic approach. Analogous problems have been studied for years by physicists extracting macroscopic states of various physical systems by examining microscopic elements and their interactions. These valuable experiences are now being extended to the medical field. From this perspective, we discuss how recent developments in statistical physics, machine learning and inference algorithms are coming together to improve current medical diagnostic approaches.

Incorporating Pharmacometrics into Pharmacoeconomic Models: Applications from Drug Development

Pharmacometrics is the science of quantifying the relationship between the pharmacokinetics and pharmacodynamics of drugs in combination with disease models and trial information to aid in drug development and dosing optimization for clinical practice. Considering the variability in the dose-concentration-effect relationship of drugs, an opportunity exists in linking pharmacokinetic and pharmacodynamic model-based estimates with pharmacoeconomic models. This link may provide early estimates of the cost effectiveness of drug therapies, thus informing late-stage drug development, pricing, and reimbursement decisions. Published case studies have demonstrated how integrated pharmacokinetic-pharmacodynamic-pharmacoeconomic models can complement traditional pharmacoeconomic analyses by identifying the impact of specific patient sub-groups, dose, dosing schedules, and adherence on the cost effectiveness of drugs, thus providing a mechanistic basis to predict the economic value of new drugs. Greater collaboration between the pharmacoeconomics and pharmacometrics community can enable methodological improvements in pharmacokinetic-pharmacodynamic-pharmacoeconomic models to support drug development.

Using medical claims database to develop a population disease progression model for leuprorelin-treated subjects with hormone-sensitive prostate cancer

Androgen deprivation therapy (ADT) is a widely used treatment for patients with hormone-sensitive prostate cancer (PCa). However, duration of treatment response varies, and most patients eventually experience disease progression despite treatment. Leuprorelin is a luteinizing hormone-releasing hormone (LHRH) agonist, a commonly used form of ADT. Prostate-specific antigen (PSA) is a biomarker for monitoring disease progression and predicting treatment response and survival in PCa. However, time-dependent profile of tumor regression and growth in patients with hormone-sensitive PCa on ADT has never been fully characterized. In this analysis, nationwide medical claims database provided by Humana from 2007 to 2011 was used to construct a population-based disease progression model for patients with hormone-sensitive PCa on leuprorelin. Data were analyzed by nonlinear mixed effects modeling utilizing Monte Carlo Parametric Expectation Maximization (MCPEM) method in NONMEM. Covariate selection was performed using a modified Wald’s approximation method with backward elimination (WAM-BE) proposed by our group. 1113 PSA observations from 264 subjects with malignant PCa were used for model development. PSA kinetics were well described by the final covariate model. Model parameters were well estimated, but large between-patient variability was observed. Hemoglobin significantly affected proportion of drug-resistant cells in the original tumor, while baseline PSA and antiandrogen use significantly affected treatment effect on drug-sensitive PCa cells (Ds). Population estimate of Ds was 3.78 x 10⁻² day^-1. Population estimates of growth rates for drug-sensitive (Gs) and drug-resistant PCa cells (G_R) were 1.96 x 10⁻³ and 6.54 x 10⁻⁴ day^-1, corresponding to a PSA doubling time of 354 and 1060 days, respectively. Proportion of the original PCa cells inherently resistant to treatment was estimated to be 1.94%. Application of population-based disease progression model to clinical data allowed characterization of tumor resistant patterns and growth/regression rates that enhances our understanding of how PCa responds to ADT.

Maximizing Knowledge Extraction From Patient-Reported Outcome Data Using Exposure-Outcome Item Response Modeling Approach: Understanding Efavirenz-Induced Central Nervous System Toxicity

This investigation was undertaken to maximally extract hidden knowledge from an efavirenz-based trial data set using an item response theory-based approach to exposure-outcome analysis. The aim was to understand the influence of efavirenz exposure on the underlying neuropsychiatric impairment in HIV/AIDS patients. Data from 196 individuals with 4136 neuropsychiatric impairment symptom observations at baseline and 2 and 12 weeks of 600-mg efavirenz-based therapy was analyzed. The 7 symptoms were categorized as sleep disorders (3), hallucinations (3), and cognitive impairment (1). A longitudinal item response theory model incorporating 3 latent variables based on the symptom categories and a linear disease progression model with a symptomatic drug effect was developed in NONMEM 7.4.1. The model adequately characterized the observed symptoms and revealed the hidden knowledge on the informativeness of symptoms in characterizing the underlying neuropsychiatric impairment. Informativeness, which was affected by underlying impairment severity and efavirenz therapy duration, varied among symptoms. Sleep disorders were the most efavirenz-sensitive symptom category. Vivid dreams and auditory hallucinations were most informative in their respective symptom categories. Mini-Mental State Examination score cutoff levels used to classify the severity of cognitive impairment did not distinctively correspond with neuropsychiatric impairment severity. Efavirenz treatment effect on the severity of neuropsychiatric impairment was not more than 15%. Simulation of individual symptoms at the 400-mg dose only reduced the prevalence of sleep disorder symptoms. Use of the exposure-item response theory modeling approach maximally extracted hidden knowledge about efavirenz-induced neuropsychiatric impairment and appropriately characterized the impact of dose reduction on specific neuropsychiatric impairment symptoms.

Disease Progression Modeling Analysis of the Change of Bone Mineral Density by Postoperative Hormone Therapies in Postmenopausal Patients With Early Breast Cancer

The osteoporosis incidence in postmenopausal patients on aromatase inhibitors (AI) is much higher than in those on tamoxifen, and adverse effects other than musculoskeletal disorders are less on AI than on tamoxifen. In this study we performed disease-progression modeling in postmenopausal patients with early breast cancer who had received 5 years of postoperative hormone therapy. Clinical data from postmenopausal patients who had received postoperative hormonal therapy and met the predefined selection criteria were retrospectively collected in an anonymized way. Disease-progression modeling and simulations were performed using NONMEM version 7.42. A first-order deterioration model with a combination of a symptomatic model (when a drug effect provides a transient bad effect by offsetting the severity of the disease) and a disease-modifying model (when a drug affects the disease progression rate) was used. Vitamin D supplementation was found to have a disease-modifying effect in osteoporosis, whereas AI decreased the bone mineral density by a t score of -0.21. However, after stopping the AI, the estrogen level reverted to normal, thereby reexercising protective effects against bone loss. In the simulation the probability of osteoporosis increased by 10% in the AI group compared with the other groups (tamoxifen, no-treatment group) during the medication period. Tamoxifen showed no significant effects in the final model.

Time Series Characteristics of Serum Branched-Chain Amino Acids for Early Diagnosis of Chronic Heart Failure

Chronic heart failure (CHF) is an ongoing clinical syndrome with cardiac dysfunction that can be traced to alterations in cardiac metabolism. The identification of metabolic biomarkers in easily accessible fluids to improve the early diagnosis of CHF has been elusive to date. In this study, we took multidimensional analytical techniques to discover potentially new diagnostic biomarkers by focusing on the dynamic changes of metabolites in serum during the progression of CHF. Using mass-spectrometry-based untargeted metabolomics, we identified 23 cardiac metabolites that were altered in a rat model of myocardial infarction induced CHF. Among these differential metabolites, branched-chain amino acids (BCAAs) in serum, especially leucine and valine, showed a high capability to differentiate between CHF and sham-operated rats, of which area under the receiver operating characteristic curve was greater than 0.75. Combining with targeted analysis of the amino acids and related proteins and genes, we confirmed that BCAA metabolic pathway was significantly inhibited in rat failing hearts. On the basis of the time series data of serum samples, we characterized the fluctuation pattern of circulating BCAAs by the disease progression model. Finally, the time-resolved diagnostic potential of serum BCAAs was evaluated by the machine-learning-based classifier, and high diagnostic accuracy of 93.75% was achieved within 3 weeks after surgery. These findings provide a promising metabolic signature that can be further exploited for CHF early diagnostic development.

Emerging roles for clinical pharmacometrics in cancer precision medicine

Purpose of review

Although significant progress has been made in cancer research, there exist unmet needs in patient care as reflected by the 'Cancer Moonshot' goals. This review appreciates the potential utility of quantitative pharmacology in cancer precision medicine.

Recent findings

Precision oncology has received federal funding largely due to 'The Precision Medicine Initiative'. Precision medicine takes into account the inter-individual variability, and allows for tailoring the right medication or the right dose of drug to the best subpopulation of patients who will likely respond to the intervention, thus enhancing therapeutic success and reducing "financial toxicity" to patients, families and caregivers. The National Cancer Institute (NCI) committed US$ 70 million from its fiscal year 2016 budget to advance precision oncology research. Through the 'Critical Path Initiative', pharmacometrics has gained an important role in drug development; however, it is yet to find widespread clinical applicability.

Summary

Stakeholders including clinicians and pharmacometricians need to work in concert to ensure that benefits of model-based approaches are harnessed to personalize cancer care to the individual needs of the patient via better dosing strategies, companion diagnostics, and predictive biomarkers. In medical oncology, where immediate patient care is the clinician's primary concern, pharmacometric approaches can be tailored to build models that rely on patient data already digitally available in the Electronic Health Record (EHR) to facilitate quick collaboration and avoid additional funding needs. Taken together, we offer a roadmap for the future of precision oncology which is fraught with both challenges and opportunities for pharmacometricians and clinicians alike.

Does transition of urinary incontinence from one subtype to another represent progression of the disease?

INTRODUCTION AND HYPOTHESIS

Mixed urinary incontinence (UI) is, on average, more severe than urgency UI or stress UI. We tested the hypothesis that mixed UI is a more advanced stage of UI by comparing transition probabilities among women with stress, urgency, and mixed UI.

METHODS

We used data from the General Longitudinal Overactive Bladder Evaluation Study-UI, which included community-dwelling women, aged 40+ years, with UI at baseline. Study participants completed two or more consecutive bladder health surveys every 6 months for up to 4 years. Using sequential 6-month surveys, transition probabilities among UI subtypes were estimated using the Cox-proportional hazards model, with the expectation that probabilities from stress or urgency UI to mixed UI would be substantially greater than probabilities in the reverse direction.

RESULTS

Among 6,993 women 40+ years of age at baseline, the number (prevalence) of women with stress, urgency, and mixed UI was 481 (6.9%), 557 (8.0%), and 1488 (21.3%) respectively. Over a 4-year period, the transition probabilities from stress UI (34%) and urgency UI (27%) to mixed UI was significantly higher than probabilities from mixed to stress UI (6%) or to urgency UI (rate = 9%). The adjusted transition hazard ratio for stress UI and urgency UI was 2.06 (95% CI: 1.73-2.92) and 1.85 (95% CI: 1.63-2.57) respectively compared with mixed UI.

CONCLUSION

The substantially higher transition from stress UI and urgency UI to mixed UI supports the hypothesis that mixed UI might represent a more advanced stage of UI that may have implications for understanding disease progression.