DeepMutation: A Neural Mutation Tool

Mutation testing can be used to assess the fault-detection capabilities of a given test suite. To this aim, two characteristics of mutation testing frameworks are of paramount importance: (i) they should generate mutants that are representative of real faults; and (ii) they should provide a complete tool chain able to automatically generate, inject, and test the mutants. To address the first point, we recently proposed an approach using a Recurrent Neural Network Encoder-Decoder architecture to learn mutants from ~787k faults mined from real programs. The empirical evaluation of this approach confirmed its ability to generate mutants representative of real faults. In this paper, we address the second point, presenting DeepMutation, a tool wrapping our deep learning model into a fully automated tool chain able to generate, inject, and test mutants learned from real faults. Video: https://sites.google.com/view/learning-mutation/deepmutationComment: Accepted to the 42nd ACM/IEEE International Conference on Software Engineering (ICSE 2020), Demonstrations Track - Seoul, South Korea, May 23-29, 2020, 4 page

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BACKGROUND: The aim of the current work was to perform a clinical trial simulation (CTS) analysis to optimize a drug-drug interaction (DDI) study of vincristine in children who also received azole antifungals, taking into account challenges of conducting clinical trials in this population, and, to provide a motivating example of the application of CTS in the design of pediatric oncology clinical trials. PROCEDURE: A pharmacokinetic (PK) model for vincristine in children was used to simulate concentration-time profiles. A continuous model for body surface area versus age was defined based on pediatric growth curves. Informative sampling time windows were derived using D-optimal design. The CTS framework was used to different magnitudes of clearance inhibition (10%, 25%, or 40%), sample size (30-500), the impact of missing samples or sampling occasions, and the age distribution, on the power to detect a significant inhibition effect, and in addition, the relative estimation error (REE) of the interaction effect. RESULTS: A minimum group specific sample size of 38 patients with a total sample size of 150 patients was required to detect a clearance inhibition effect of 40% with 80% power, while in the case of a lower effect of clearance inhibition, a substantially larger sample size was required. However, for the majority of re-estimated drug effects, the inhibition effect could be estimated precisely (REE < 25%) in even smaller sample sizes and with lower effect sizes. CONCLUSION: This work demonstrated the utility of CTS for the evaluation of PK clinical trial designs in the pediatric oncology population

Quantitative systems modeling approaches towards model-informed drug development: Perspective through case studies

Quantitative systems pharmacology (QSP) modeling has become an increasingly popular approach impacting our understanding of disease mechanisms and helping predict patients’ treatment responses to facilitate study design or development go/no-go decisions. In this paper, we highlight the notable contributions and opportunities that QSP approaches are to offer during the drug development process by sharing three examples that have facilitated internal decisions. The barriers to successful applications and the factors that facilitate the success of the modeling approach is discussed

A retrospective claims analysis of combination therapy in the treatment of adult attention-deficit/hyperactivity disorder (ADHD)

<p>Abstract</p> <p>Background</p> <p>Combination therapy in managing psychiatric disorders is not uncommon. While combination therapy has been documented for depression and schizophrenia, little is known about combination therapy practices in managing attention-deficit/hyperactivity disorder (ADHD). This study seeks to quantify the combination use of ADHD medications and to understand predictors of combination therapy.</p> <p>Methods</p> <p>Prescription dispensing events were drawn from a U.S. national claims database including over 80 managed-care plans. Patients studied were age 18 or over with at least 1 medical claim with a diagnosis of ADHD (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code 314.0), a pharmacy claim for ADHD medication during the study period July2003 to June2004, and continuous enrollment 6 months prior to and throughout the study period. Dispensing events were grouped into 6 categories: atomoxetine (ATX), long-acting stimulants (LAS), intermediate-acting stimulants (IAS), short-acting stimulants (SAS), bupropion (BUP), and Alpha-2 Adrenergic Agonists (A2A). Events were assigned to calendar months, and months with combined use from multiple categories within patient were identified. Predictors of combination therapy for LAS and for ATX were modeled for patients covered by commercial plans using logistic regression in a generalized estimating equations framework to adjust for within-patient correlation between months of observation. Factors included age, gender, presence of the hyperactive component of ADHD, prior diagnoses for psychiatric disorders, claims history of recent psychiatric visit, insurance plan type, and geographic region.</p> <p>Results</p> <p>There were 18,609 patients identified representing a total of 11,886 months of therapy with ATX; 40,949 months with LAS; 13,622 months with IAS; 38,141 months with SAS; 22,087 months with BUP; and 1,916 months with A2A. Combination therapy was present in 19.7% of continuing months (months after the first month of therapy) for ATX, 21.0% for LAS, 27.4% for IAS, 23.1% for SAS, 36.9% for BUP, and 53.0% for A2A.</p> <p>For patients receiving LAS, being age 25–44 or age 45 and older versus being 18–24 years old, seeing a psychiatrist, having comorbid depression, or having point-of-service coverage versus a Health Maintenance Organization (HMO) resulted in odds ratios significantly greater than 1, representing increased likelihood for combination therapy in managing adult ADHD.</p> <p>For patients receiving ATX, being age 25–44 or age 45 and older versus being 18–24 years old, seeing a psychiatrist, having a hyperactive component to ADHD, or having comorbid depression resulted in odds ratios significantly greater than 1, representing increased likelihood for combination therapy in managing adult ADHD.</p> <p>Conclusion</p> <p>ATX and LAS are the most likely drugs to be used as monotherapy. Factors predicting combination use were similar for months in which ATX was used and for months in which LAS was used except that a hyperactive component to ADHD predicted increased combination use for ATX but not for LAS.</p

Methylphenidate Exposure Induces Dopamine Neuron Loss and Activation of Microglia in the Basal Ganglia of Mice

Background: Methylphenidate (MPH) is a psychostimulant that exerts its pharmacological effects via preferential blockade of the dopamine transporter (DAT) and the norepinephrine transporter (NET), resulting in increased monoamine levels in the synapse. Clinically, methylphenidate is prescribed for the symptomatic treatment of ADHD and narcolepsy; although lately, there has been an increased incidence of its use in individuals not meeting the criteria for these disorders. MPH has also been misused as a ‘‘cognitive enhancer’ ’ and as an alternative to other psychostimulants. Here, we investigate whether chronic or acute administration of MPH in mice at either 1 mg/kg or 10 mg/kg, affects cell number and gene expression in the basal ganglia. Methodology/Principal Findings: Through the use of stereological counting methods, we observed a significant reduction (,20%) in dopamine neuron numbers in the substantia nigra pars compacta (SNpc) following chronic administration of 10 mg/kg MPH. This dosage of MPH also induced a significant increase in the number of activated microglia in the SNpc. Additionally, exposure to either 1 mg/kg or 10 mg/kg MPH increased the sensitivity of SNpc dopaminergic neurons to the parkinsonian agent 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Unbiased gene screening employing Affymetrix GeneChipH HT MG-430 PM revealed changes in 115 and 54 genes in the substantia nigra (SN) of mice exposed to 1 mg/kg and 10 mg/kg MPH doses, respectively. Decreases in the mRNA levels of gdnf, dat1, vmat2, and th in the substantia nigr

Designing antifilarial drug trials using clinical trial simulators

Lymphatic filariasis and onchocerciasis are neglected tropical diseases (NTDs) targeted for elimination by mass (antifilarial) drug administration. These drugs are predominantly active against the microfilarial progeny of adult worms. New drugs or combinations are needed to improve patient therapy and to enhance the effectiveness of interventions in persistent hotspots of transmission. Several therapies and regimens are currently in (pre-)clinical testing. Clinical trial simulators (CTSs) project patient outcomes to inform the design of clinical trials but have not been widely applied to NTDs, where their resource-saving payoffs could be highly beneficial. We demonstrate the utility of CTSs using our individual-based onchocerciasis transmission model (EPIONCHO-IBM) that projects trial outcomes of a hypothetical macrofilaricidal drug. We identify key design decisions that influence the power of clinical trials, including participant eligibility criteria and post-treatment follow-up times for measuring infection indicators. We discuss how CTSs help to inform target product profiles

Pharmacokinetic-Pharmacodynamic Modeling in Pediatric Drug Development, and the Importance of Standardized Scaling of Clearance.

Pharmacokinetic/pharmacodynamic (PKPD) modeling is important in the design and conduct of clinical pharmacology research in children. During drug development, PKPD modeling and simulation should underpin rational trial design and facilitate extrapolation to investigate efficacy and safety. The application of PKPD modeling to optimize dosing recommendations and therapeutic drug monitoring is also increasing, and PKPD model-based dose individualization will become a core feature of personalized medicine. Following extensive progress on pediatric PK modeling, a greater emphasis now needs to be placed on PD modeling to understand age-related changes in drug effects. This paper discusses the principles of PKPD modeling in the context of pediatric drug development, summarizing how important PK parameters, such as clearance (CL), are scaled with size and age, and highlights a standardized method for CL scaling in children. One standard scaling method would facilitate comparison of PK parameters across multiple studies, thus increasing the utility of existing PK models and facilitating optimal design of new studies

A review of methods for comparing treatments evaluated in studies which form disconnected networks of evidence

A network meta-analysis allows a simultaneous comparison between treatments evaluated in randomised controlled trials that share at least one treatment with at least one other study. Estimates of treatment effects may be required for treatments across disconnected networks of evidence, which requires a different statistical approach and modelling assumptions to account for imbalances in prognostic variables and treatment effect modifiers between studies. In this paper, we review and discuss methods for comparing treatments evaluated in studies that form disconnected networks of evidence. Several methods have been proposed but assessing which are appropriate often depends on the clinical context as well as the availability of data. Most methods account for sampling variation but do not always account for others sources of uncertainty. We suggest that further research is required to assess the properties of methods and the use of approaches that allow the incorporation of external information to reflect parameter and structural uncertainty

Prediction of Disease Progression, Treatment Response and Dropout in Chronic Obstructive Pulmonary Disease (COPD)

Drug development in chronic obstructive pulmonary disease (COPD) has been characterised by unacceptably high failure rates. In addition to the poor sensitivity in forced expiratory volume in one second (FEV1), numerous causes are known to contribute to this phenomenon, which can be clustered into drug-, disease- and design-related factors. Here we present a model-based approach to describe disease progression, treatment response and dropout in clinical trials with COPD patients