Biosignature Discovery for Substance Use Disorders Using Statistical Learning

Is There Such a Thing as a Biosignature?

The concept of a biosignature is widely used in astrobiology to suggest a link between some observation and a biological cause, given some context. The term itself has been defined and used in several ways in different parts of the scientific community involved in the search for past or present life on Earth and beyond. With the ongoing acceleration in the search for life in distant time and/or deep space, there is a need for clarity and accuracy in the formulation and reporting of claims. Here, we critically review the biosignature concept(s) and the associated nomenclature in light of several problems and ambiguities emphasized by recent works. One worry is that these terms and concepts may imply greater certainty than is usually justified by a rational interpretation of the data. A related worry is that terms such as "biosignature" may be inherently misleading, for example, because the divide between life and non-life-and their observable effects-is fuzzy. Another worry is that different parts of the multidisciplinary community may use non-equivalent or conflicting definitions and conceptions, leading to avoidable confusion. This review leads us to identify a number of pitfalls and to suggest how they can be circumvented. In general, we conclude that astrobiologists should exercise particular caution in deciding whether and how to use the concept of biosignature when thinking and communicating about habitability or life. Concepts and terms should be selected carefully and defined explicitly where appropriate. This would improve clarity and accuracy in the formulation of claims and subsequent technical and public communication about some of the most profound and important questions in science and society. With this objective in mind, we provide a checklist of questions that scientists and other interested parties should ask when assessing any reported detection of a "biosignature" to better understand exactly what is being claimed.

Predicting nicotine metabolism across ancestries using genotypes

BACKGROUND

There is a need to match characteristics of tobacco users with cessation treatments and risks of tobacco attributable diseases such as lung cancer. The rate in which the body metabolizes nicotine has proven an important predictor of these outcomes. Nicotine metabolism is primarily catalyzed by the enzyme cytochrone P450 (CYP2A6) and CYP2A6 activity can be measured as the ratio of two nicotine metabolites: trans-3'-hydroxycotinine to cotinine (NMR). Measurements of these metabolites are only possible in current tobacco users and vary by biofluid source, timing of collection, and protocols; unfortunately, this has limited their use in clinical practice. The NMR depends highly on genetic variation near CYP2A6 on chromosome 19 as well as ancestry, environmental, and other genetic factors. Thus, we aimed to develop prediction models of nicotine metabolism using genotypes and basic individual characteristics (age, gender, height, and weight).

RESULTS

We identified four multiethnic studies with nicotine metabolites and DNA samples. We constructed a 263 marker panel from filtering genome-wide association scans of the NMR in each study. We then applied seven machine learning techniques to train models of nicotine metabolism on the largest and most ancestrally diverse dataset (N=2239). The models were then validated using the other three studies (total N=1415). Using cross-validation, we found the correlations between the observed and predicted NMR ranged from 0.69 to 0.97 depending on the model. When predictions were averaged in an ensemble model, the correlation was 0.81. The ensemble model generalizes well in the validation studies across ancestries, despite differences in the measurements of NMR between studies, with correlations of: 0.52 for African ancestry, 0.61 for Asian ancestry, and 0.46 for European ancestry. The most influential predictors of NMR identified in more than two models were rs56113850, rs11878604, and 21 other genetic variants near CYP2A6 as well as age and ancestry.

CONCLUSIONS

We have developed an ensemble of seven models for predicting the NMR across ancestries from genotypes and age, gender and BMI. These models were validated using three datasets and associate with nicotine dosages. The knowledge of how an individual metabolizes nicotine could be used to help select the optimal path to reducing or quitting tobacco use, as well as, evaluating risks of tobacco use.

Predicting Relapse in Substance Use: Prospective Modeling Based on Intensive Longitudinal Data on Mental Health, Cognition, and Craving

Patients with severe substance use disorders are often characterized by neurocognitive impairments and elevated mental health symptom load, typically associated with craving intensity and substance use relapse. There is a need to improve the predictive capabilities of when relapse occurs in order to improve substance use treatment. The current paper contains data from 19 patients (seven females) in a long-term inpatient substance use treatment setting over the course of several weeks, with up to three weekly data collections. We collected data from 252 sessions, ranging from 1 to 24 sessions per subject. The subjects reported craving, self-control, and mental health on each occasion. Before starting the repeated data collection, a baseline neuropsychological screening was performed. In this repeated-measures prospective study, the mixed-effects models with time-lagged predictors support a model of substance use craving and relapse being predicted by the baseline reaction time as well as the temporal changes and variability in mental health symptom load, self-control, and craving intensity with moderate to high effect sizes. This knowledge may contribute to more personalized risk assessments and treatments for this group of patients.

Multiethnic Prediction of Nicotine Biomarkers and Association With Nicotine Dependence

INTRODUCTION

The nicotine metabolite ratio and nicotine equivalents are measures of metabolism rate and intake. Genome-wide prediction of these nicotine biomarkers in multiethnic samples will enable tobacco-related biomarker, behavioral, and exposure research in studies without measured biomarkers.

AIMS AND METHODS

We screened genetic variants genome-wide using marginal scans and applied statistical learning algorithms on top-ranked genetic variants, age, ethnicity and sex, and, in additional modeling, cigarettes per day (CPD), (in additional modeling) to build prediction models for the urinary nicotine metabolite ratio (uNMR) and creatinine-standardized total nicotine equivalents (TNE) in 2239 current cigarette smokers in five ethnic groups. We predicted these nicotine biomarkers using model ensembles and evaluated external validity using dependence measures in 1864 treatment-seeking smokers in two ethnic groups.

RESULTS

The genomic regions with the most selected and included variants for measured biomarkers were chr19q13.2 (uNMR, without and with CPD) and chr15q25.1 and chr10q25.3 (TNE, without and with CPD). We observed ensemble correlations between measured and predicted biomarker values for the uNMR and TNE without (with CPD) of 0.67 (0.68) and 0.65 (0.72) in the training sample. We observed inconsistency in penalized regression models of TNE (with CPD) with fewer variants at chr15q25.1 selected and included. In treatment-seeking smokers, predicted uNMR (without CPD) was significantly associated with CPD and predicted TNE (without CPD) with CPD, time-to-first-cigarette, and Fagerström total score.

CONCLUSIONS

Nicotine metabolites, genome-wide data, and statistical learning approaches developed novel robust predictive models for urinary nicotine biomarkers in multiple ethnic groups. Predicted biomarker associations helped define genetically influenced components of nicotine dependence.

IMPLICATIONS

We demonstrate development of robust models and multiethnic prediction of the uNMR and TNE using statistical and machine learning approaches. Variants included in trained models for nicotine biomarkers include top-ranked variants in multiethnic genome-wide studies of smoking behavior, nicotine metabolites, and related disease. Association of the two predicted nicotine biomarkers with Fagerström Test for Nicotine Dependence items supports models of nicotine biomarkers as predictors of physical dependence and nicotine exposure. Predicted nicotine biomarkers may facilitate tobacco-related disease and treatment research in samples with genomic data and limited nicotine metabolite or tobacco exposure data.

Noninvasive and minimally invasive techniques for the diagnosis and management of allergic diseases

Allergic diseases of the (upper and lower) airways, the skin and the gastrointestinal tract, are on the rise, resulting in impaired quality of life, decreased productivity, and increased healthcare costs. As allergic diseases are mostly tissue-specific, local sampling methods for respective biomarkers offer the potential for increased sensitivity and specificity. Additionally, local sampling using noninvasive or minimally invasive methods can be cost-effective and well tolerated, which may even be suitable for primary or home care sampling. Non- or minimally invasive local sampling and diagnostics may enable a more thorough endotyping, may help to avoid under- or overdiagnosis, and may provide the possibility to approach precision prevention, due to early diagnosis of these local diseases even before they get systemically manifested and detectable. At the same time, dried blood samples may help to facilitate minimal-invasive primary or home care sampling for classical systemic diagnostic approaches. This EAACI position paper contains a thorough review of the various technologies in allergy diagnosis available on the market, which analytes or biomarkers are employed, and which samples or matrices can be used. Based on this assessment, EAACI position is to drive these developments to efficiently identify allergy and possibly later also viral epidemics and take advantage of comprehensive knowledge to initiate preventions and treatments.

Evaluation of a weighted genetic risk score for the prediction of biomarkers of CYP2A6 activity

The nicotine metabolite ratio (NMR; 3-hydroxycotinine/cotinine) is an index of CYP2A6 activity. CYP2A6 is responsible for nicotine's metabolic inactivation and variation in the NMR/CYP2A6 is associated with several smoking behaviors. Our aim was to integrate established alleles and novel genome-wide association studies (GWAS) signals to create a weighted genetic risk score (wGRS) for the CYP2A6 gene for European-ancestry populations. The wGRS was compared with a previous CYP2A6 gene scoring approach designed for an alternative phenotype (C2/N2; cotinine-d2/(nicotine-d2 + cotinine-d2)). CYP2A6 genotypes and the NMR were assessed in European-ancestry participants. The wGRS training set included N = 933 smokers recruited to the Pharmacogenetics of Nicotine Addiction and Treatment clinical trial [NCT01314001]. The replication cohort included N = 196 smokers recruited to the Quit 2 Live clinical trial [NCT01836276]. Comparisons between the two CYP2A6 phenotypes and with fractional clearance were made in a laboratory-based pharmacokinetic study (N = 92 participants). In both the training and replication sets, the wGRS, which included seven CYP2A6 variants, explained 33.8% (P < 0.001) of the variance in NMR, providing improved predictive power to the NMR phenotype when compared with other CYP2A6 gene scoring approaches. NMR and C2/N2 were strongly correlated to nicotine clearance (ρ = 0.70 and ρ = 0.79, respectively; P < 0.001), and to one another (ρ = 0.82; P < 0.001); however reduced function genotypes occurred in slow NMR but throughout C2/N2. The wGRS was able to predict smoking quantity and nicotine intake, to discriminate between NMR slow and normal metabolizers (AUC = 0.79; P < 0.001), and to replicate previous NMR-stratified cessation outcomes showing unique treatment outcomes between metabolizer groups.

Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges

The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.

Pharmacogenetics of alcohol use disorder treatments: an update

Introduction: Alcohol use disorder (AUD) is highly prevalent; costly economically, socially, and interpersonally; and grossly undertreated. The low rate of utilization of medications with demonstrated (albeit modest) efficacy is particularly noteworthy. One approach to increasing the utility and safety of available medications is to use a precision medicine approach, which seeks to identify patients for whom specific medications are likely to be most efficacious and have the fewest adverse effects. Areas Covered: We review the literature on the pharmacogenetics of AUD treatment using both approved and off-label medications. We cover both laboratory studies and clinical trials, highlighting valuable mechanistic insights and underscoring the potential value of precision-based care for AUD. Expert Opinion: Pharmacotherapy can be a useful component of AUD treatment. Currently, the evidence regarding genetic predictors of medication efficacy is very limited. Thus, a precision medicine approach is not yet ready for widespread clinical implementation. Further research is needed to identify candidate genetic variants that moderate the response to both established and novel medications. The growing availability of large-scale, longitudinal datasets that enable the synthesis of genetic and electronic health record data provides important opportunities to develop this area of research.