Artificial intelligence and machine learning in clinical pharmacological research.

BACKGROUND: Clinical pharmacology research has always involved computational analysis. With the abundance of drug-related data available, the integration of artificial intelligence (AI) and machine learning (ML) methods has emerged as a promising way to enhance clinical pharmacology research. METHODS: Based on an accepted definition of clinical pharmacology as a field of research dealing with all aspects of drug-human interactions, the analysis included publications from institutes specializing in clinical pharmacology. Research topics and the most used machine learning methods in clinical pharmacology were retrieved from the PubMed database and summarized. RESULTS: ML was identified in 674 publications attributed to clinical pharmacology research, with a significant increase in publication activity over the last decade. Notable research topics addressed by ML/AI included Covid-19-related clinical pharmacology research, clinical neuropharmacology, drug safety and risk assessment, clinical pharmacology related to cancer research, and antimicrobial and antiviral research unrelated to Covid-19. In terms of ML methods, neural networks, random forests, and support vector machines were frequently mentioned in the abstracts of the retrieved papers. CONCLUSIONS: ML, and AI in general, is increasingly being used in various research areas within clinical pharmacology. This report presents specific examples of applications and highlights the most used ML methods.

Diagnosed and subjectively perceived long-term effects of COVID-19 infection on olfactory function assessed by supervised machine learning.

Loss of olfactory function is a typical acute coronavirus disease 2019 (COVID-19) symptom, at least in early variants of SARS-CoV2. The time that has elapsed since the emergence of COVID-19 now allows for assessing the long-term prognosis of its olfactory impact. Participants (n = 722) of whom n = 464 reported having had COVID-19 dating back with a mode of 174 days were approached in a museum as a relatively unbiased environment. Olfactory function was diagnosed by assessing odor threshold and odor identification performance. Subjects also rated their actual olfactory function on an 11-point numerical scale [0,…10]. Neither the frequency of olfactory diagnostic categories nor olfactory test scores showed any COVID-19-related effects. Olfactory diagnostic categories (anosmia, hyposmia, or normosmia) were similarly distributed among former patients and controls (0.86%, 18.97%, and 80.17% for former patients and 1.17%, 17.51%, and 81.32% for controls). Former COVID-19 patients, however, showed differences in their subjective perception of their own olfactory function. The impact of this effect was substantial enough that supervised machine learning algorithms detected past COVID-19 infections in new subjects, based on reduced self-awareness of olfactory performance and parosmia, while the diagnosed olfactory function did not contribute any relevant information in this context. Based on diagnosed olfactory function, results suggest a positive prognosis for COVID-19-related olfactory loss in the long term. Traces of former infection are found in self-perceptions of olfaction, highlighting the importance of investigating the long-term effects of COVID-19 using reliable and validated diagnostic measures in olfactory testing.

Machine learning analysis predicts a person's sex based on mechanical but not thermal pain thresholds.

Sex differences in pain perception have been extensively studied, but precision medicine applications such as sex-specific pain pharmacology have barely progressed beyond proof-of-concept. A data set of pain thresholds to mechanical (blunt and punctate pressure) and thermal (heat and cold) stimuli applied to non-sensitized and sensitized (capsaicin, menthol) forearm skin of 69 male and 56 female healthy volunteers was analyzed for data structures contingent with the prior sex structure using unsupervised and supervised approaches. A working hypothesis that the relevance of sex differences could be approached via reversibility of the association, i.e., sex should be identifiable from pain thresholds, was verified with trained machine learning algorithms that could infer a person's sex in a 20% validation sample not seen to the algorithms during training, with balanced accuracy of up to 79%. This was only possible with thresholds for mechanical stimuli, but not for thermal stimuli or sensitization responses, which were not sufficient to train an algorithm that could assign sex better than by guessing or when trained with nonsense (permuted) information. This enabled the translation to the molecular level of nociceptive targets that convert mechanical but not thermal information into signals interpreted as pain, which could eventually be used for pharmacological precision medicine approaches to pain. By exploiting a key feature of machine learning, which allows for the recognition of data structures and the reduction of information to the minimum relevant, experimental human pain data could be characterized in a way that incorporates "non" logic that could be translated directly to the molecular pharmacological level, pointing toward sex-specific precision medicine for pain.

Pharmacological data science perspective on fatal incidents of morphine treatment.

Morphine prescribed for analgesia has caused drug-related deaths at an estimated incidence of 0.3% to 4%. Morphine has pharmacological properties that make it particularly difficult to assess the causality of morphine administration with a patient's death, such as its slow transfer between plasma and central nervous sites of action and the existence of the active metabolite morphine-6-glucuronide with opioid agonistic effects, Furthermore, there is no well-defined toxic dose or plasma/blood concentration for morphine. Dosing is often adjusted for adequate pain relief. Here, we summarize reported deaths associated with morphine therapy, including associated morphine exposure and modulating patient factors such as pharmacogenetics, concomitant medications, or comorbidities. In addition, we systematically analyzed published numerical information on the stability of concentrations of morphine and its relevant metabolites in biological samples collected postmortem. A medicolegal case is presented in which the causality of morphine administration with death was in dispute and pharmacokinetic modeling was applied to infer the administered dose. The results of this analytical review suggest that (i) inference from postmortem blood concentrations to the morphine dose administered has low validity and (ii) causality between a patient's death and the morphine dose administered remains a highly context-dependent and collaborative assessment among experts from different medical specialties.

Artificial intelligence and machine learning in pain research: a data scientometric analysis.

The collection of increasing amounts of data in health care has become relevant for pain therapy and research. This poses problems for analyses with classical approaches, which is why artificial intelligence (AI) and machine learning (ML) methods are being included into pain research. The current literature on AI and ML in the context of pain research was automatically searched and manually curated. Common machine learning methods and pain settings covered were evaluated. Further focus was on the origin of the publication and technical details, such as the included sample sizes of the studies analyzed with ML. Machine learning was identified in 475 publications from 18 countries, with 79% of the studies published since 2019. Most addressed pain conditions included low back pain, musculoskeletal disorders, osteoarthritis, neuropathic pain, and inflammatory pain. Most used ML algorithms included random forests and support vector machines; however, deep learning was used when medical images were involved in the diagnosis of painful conditions. Cohort sizes ranged from 11 to 2,164,872, with a mode at n = 100; however, deep learning required larger data sets often only available from medical images. Artificial intelligence and ML, in particular, are increasingly being applied to pain-related data. This report presents application examples and highlights advantages and limitations, such as the ability to process complex data, sometimes, but not always, at the cost of big data requirements or black-box decisions.

Drugs and Epigenetic Molecular Functions. A Pharmacological Data Scientometric Analysis.

Interactions of drugs with the classical epigenetic mechanism of DNA methylation or histone modification are increasingly being elucidated mechanistically and used to develop novel classes of epigenetic therapeutics. A data science approach is used to synthesize current knowledge on the pharmacological implications of epigenetic regulation of gene expression. Computer-aided knowledge discovery for epigenetic implications of current approved or investigational drugs was performed by querying information from multiple publicly available gold-standard sources to (i) identify enzymes involved in classical epigenetic processes, (ii) screen original biomedical scientific publications including bibliometric analyses, (iii) identify drugs that interact with epigenetic enzymes, including their additional non-epigenetic targets, and (iv) analyze computational functional genomics of drugs with epigenetic interactions. PubMed database search yielded 3051 hits on epigenetics and drugs, starting in 1992 and peaking in 2016. Annual citations increased to a plateau in 2000 and show a downward trend since 2008. Approved and investigational drugs in the DrugBank database included 122 compounds that interacted with 68 unique epigenetic enzymes. Additional molecular functions modulated by these drugs included other enzyme interactions, whereas modulation of ion channels or G-protein-coupled receptors were underrepresented. Epigenetic interactions included (i) drug-induced modulation of DNA methylation, (ii) drug-induced modulation of histone conformations, and (iii) epigenetic modulation of drug effects by interference with pharmacokinetics or pharmacodynamics. Interactions of epigenetic molecular functions and drugs are mutual. Recent research activities on the discovery and development of novel epigenetic therapeutics have passed successfully, whereas epigenetic effects of non-epigenetic drugs or epigenetically induced changes in the targets of common drugs have not yet received the necessary systematic attention in the context of pharmacological plasticity.

Computational Functional Genomics-Based AmpliSeq™ Panel for Next-Generation Sequencing of Key Genes of Pain.

The genetic background of pain is becoming increasingly well understood, which opens up possibilities for predicting the individual risk of persistent pain and the use of tailored therapies adapted to the variant pattern of the patient's pain-relevant genes. The individual variant pattern of pain-relevant genes is accessible via next-generation sequencing, although the analysis of all "pain genes" would be expensive. Here, we report on the development of a cost-effective next generation sequencing-based pain-genotyping assay comprising the development of a customized AmpliSeq™ panel and bioinformatics approaches that condensate the genetic information of pain by identifying the most representative genes. The panel includes 29 key genes that have been shown to cover 70% of the biological functions exerted by a list of 540 so-called "pain genes" derived from transgenic mice experiments. These were supplemented by 43 additional genes that had been independently proposed as relevant for persistent pain. The functional genomics covered by the resulting 72 genes is particularly represented by mitogen-activated protein kinase of extracellular signal-regulated kinase and cytokine production and secretion. The present genotyping assay was established in 61 subjects of Caucasian ethnicity and investigates the functional role of the selected genes in the context of the known genetic architecture of pain without seeking functional associations for pain. The assay identified a total of 691 genetic variants, of which many have reports for a clinical relevance for pain or in another context. The assay is applicable for small to large-scale experimental setups at contemporary genotyping costs.

Machine-Learned Association of Next-Generation Sequencing-Derived Variants in Thermosensitive Ion Channels Genes with Human Thermal Pain Sensitivity Phenotypes.

Genetic association studies have shown their usefulness in assessing the role of ion channels in human thermal pain perception. We used machine learning to construct a complex phenotype from pain thresholds to thermal stimuli and associate it with the genetic information derived from the next-generation sequencing (NGS) of 15 ion channel genes which are involved in thermal perception, including ASIC1, ASIC2, ASIC3, ASIC4, TRPA1, TRPC1, TRPM2, TRPM3, TRPM4, TRPM5, TRPM8, TRPV1, TRPV2, TRPV3, and TRPV4. Phenotypic information was complete in 82 subjects and NGS genotypes were available in 67 subjects. A network of artificial neurons, implemented as emergent self-organizing maps, discovered two clusters characterized by high or low pain thresholds for heat and cold pain. A total of 1071 variants were discovered in the 15 ion channel genes. After feature selection, 80 genetic variants were retained for an association analysis based on machine learning. The measured performance of machine learning-mediated phenotype assignment based on this genetic information resulted in an area under the receiver operating characteristic curve of 77.2%, justifying a phenotype classification based on the genetic information. A further item categorization finally resulted in 38 genetic variants that contributed most to the phenotype assignment. Most of them (10) belonged to the TRPV3 gene, followed by TRPM3 (6). Therefore, the analysis successfully identified the particular importance of TRPV3 and TRPM3 for an average pain phenotype defined by the sensitivity to moderate thermal stimuli.

Machine-learned analysis of global and glial/opioid intersection-related DNA methylation in patients with persistent pain after breast cancer surgery.

BACKGROUND: Glial cells in the central nervous system play a key role in neuroinflammation and subsequent central sensitization to pain. They are therefore involved in the development of persistent pain. One of the main sites of interaction of the immune system with persistent pain has been identified as neuro-immune crosstalk at the glial-opioid interface. The present study examined a potential association between the DNA methylation of two key players of glial/opioid intersection and persistent postoperative pain. METHODS: In a cohort of 140 women who had undergone breast cancer surgery, and were assigned based on a 3-year follow-up to either a persistent or non-persistent pain phenotype, the role of epigenetic regulation of key players in the glial-opioid interface was assessed. The methylation of genes coding for the Toll-like receptor 4 (TLR4) as a major mediator of glial contributions to persistent pain or for the µ-opioid receptor (OPRM1) was analyzed and its association with the pain phenotype was compared with that conferred by global genome-wide DNA methylation assessed via quantification of the methylation in the retrotransposon LINE1. RESULTS: Training of machine learning algorithms indicated that the global DNA methylation provided a similar diagnostic accuracy for persistent pain as previously established non-genetic predictors. However, the diagnosis can be based on a single DNA based marker. By contrast, the methylation of TLR4 or OPRM1 genes could not contribute further to the allocation of the patients to the pain-related phenotype groups. CONCLUSIONS: While clearly supporting a predictive utility of epigenetic testing, the present analysis cannot provide support for specific epigenetic modulation of persistent postoperative pain via methylation of two key genes of the glial-opioid interface.

Machine-learned analysis of the association of next-generation sequencing-based genotypes with persistent pain after breast cancer surgery.

Cancer and its surgical treatment are among the most important triggering events for persistent pain, but additional factors need to be present for the clinical manifestation, such as variants in pain-relevant genes. In a cohort of 140 women undergoing breast cancer surgery, assigned based on a 3-year follow-up to either a persistent or nonpersistent pain phenotype, next-generation sequencing was performed for 77 genes selected for known functional involvement in persistent pain. Applying machine-learning and item categorization techniques, 21 variants in 13 different genes were found to be relevant to the assignment of a patient to either the persistent pain or the nonpersistent pain phenotype group. In descending order of importance for correct group assignment, the relevant genes comprised DRD1, FAAH, GCH1, GPR132, OPRM1, DRD3, RELN, GABRA5, NF1, COMT, TRPA1, ABHD6, and DRD4, of which one in the DRD4 gene was a novel discovery. Particularly relevant variants were found in the DRD1 and GPR132 genes, or in a cis-eCTL position of the OPRM1 gene. Supervised machine-learning-based classifiers, trained with 2/3 of the data, identified the correct pain phenotype group in the remaining 1/3 of the patients at accuracies and areas under the receiver operator characteristic curves of 65% to 72%. When using conservative classical statistical approaches, none of the variants passed α-corrected testing. The present data analysis approach, using machine learning and training artificial intelligences, provided biologically plausible results and outperformed classical approaches to genotype-phenotype association.

Machine Learning in Human Olfactory Research.

The complexity of the human sense of smell is increasingly reflected in complex and high-dimensional data, which opens opportunities for data-driven approaches that complement hypothesis-driven research. Contemporary developments in computational and data science, with its currently most popular implementation as machine learning, facilitate complex data-driven research approaches. The use of machine learning in human olfactory research included major approaches comprising 1) the study of the physiology of pattern-based odor detection and recognition processes, 2) pattern recognition in olfactory phenotypes, 3) the development of complex disease biomarkers including olfactory features, 4) odor prediction from physico-chemical properties of volatile molecules, and 5) knowledge discovery in publicly available big databases. A limited set of unsupervised and supervised machine-learned methods has been used in these projects, however, the increasing use of contemporary methods of computational science is reflected in a growing number of reports employing machine learning for human olfactory research. This review provides key concepts of machine learning and summarizes current applications on human olfactory data.

Development of an AmpliSeqTM Panel for Next-Generation Sequencing of a Set of Genetic Predictors of Persisting Pain.

Background: Many gene variants modulate the individual perception of pain and possibly also its persistence. The limited selection of single functional variants is increasingly being replaced by analyses of the full coding and regulatory sequences of pain-relevant genes accessible by means of next generation sequencing (NGS). Methods: An NGS panel was created for a set of 77 human genes selected following different lines of evidence supporting their role in persisting pain. To address the role of these candidate genes, we established a sequencing assay based on a custom AmpliSeqTM panel to assess the exomic sequences in 72 subjects of Caucasian ethnicity. To identify the systems biology of the genes, the biological functions associated with these genes were assessed by means of a computational over-representation analysis. Results: Sequencing generated a median of 2.85 ⋅ 106 reads per run with a mean depth close to 200 reads, mean read length of 205 called bases and an average chip loading of 71%. A total of 3,185 genetic variants were called. A computational functional genomics analysis indicated that the proposed NGS gene panel covers biological processes identified previously as characterizing the functional genomics of persisting pain. Conclusion: Results of the NGS assay suggested that the produced nucleotide sequences are comparable to those earned with the classical Sanger sequencing technique. The assay is applicable for small to large-scale experimental setups to target the accessing of information about any nucleotide within the addressed genes in a study cohort.

Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy.

BACKGROUND: Prevention of persistent pain following breast cancer surgery, via early identification of patients at high risk, is a clinical need. Supervised machine-learning was used to identify parameters that predict persistence of significant pain. METHODS: Over 500 demographic, clinical and psychological parameters were acquired up to 6 months after surgery from 1,000 women (aged 28-75 years) who were treated for breast cancer. Pain was assessed using an 11-point numerical rating scale before surgery and at months 1, 6, 12, 24, and 36. The ratings at months 12, 24, and 36 were used to allocate patents to either "persisting pain" or "non-persisting pain" groups. Unsupervised machine learning was applied to map the parameters to these diagnoses. RESULTS: A symbolic rule-based classifier tool was created that comprised 21 single or aggregated parameters, including demographic features, psychological and pain-related parameters, forming a questionnaire with "yes/no" items (decision rules). If at least 10 of the 21 rules applied, persisting pain was predicted at a cross-validated accuracy of 86% and a negative predictive value of approximately 95%. CONCLUSIONS: The present machine-learned analysis showed that, even with a large set of parameters acquired from a large cohort, early identification of these patients is only partly successful. This indicates that more parameters are needed for accurate prediction of persisting pain. However, with the current parameters it is possible, with a certainty of almost 95%, to exclude the possibility of persistent pain developing in a woman being treated for breast cancer.

Computational functional genomics-based approaches in analgesic drug discovery and repurposing.

Persistent pain is a major healthcare problem affecting a fifth of adults worldwide with still limited treatment options. The search for new analgesics increasingly includes the novel research area of functional genomics, which combines data derived from various processes related to DNA sequence, gene expression or protein function and uses advanced methods of data mining and knowledge discovery with the goal of understanding the relationship between the genome and the phenotype. Its use in drug discovery and repurposing for analgesic indications has so far been performed using knowledge discovery in gene function and drug target-related databases; next-generation sequencing; and functional proteomics-based approaches. Here, we discuss recent efforts in functional genomics-based approaches to analgesic drug discovery and repurposing and highlight the potential of computational functional genomics in this field including a demonstration of the workflow using a novel R library 'dbtORA'.

Machine-learned analysis of the association of next-generation sequencing-based human TRPV1 and TRPA1 genotypes with the sensitivity to heat stimuli and topically applied capsaicin.

Heat pain and its modulation by capsaicin varies among subjects in experimental and clinical settings. A plausible cause is a genetic component, of which TRPV1 ion channels, by their response to both heat and capsaicin, are primary candidates. However, TRPA1 channels can heterodimerize with TRPV1 channels and carry genetic variants reported to modulate heat pain sensitivity. To address the role of these candidate genes in capsaicin-induced hypersensitization to heat, pain thresholds acquired before and after topical application of capsaicin and TRPA1/TRPV1 exomic sequences derived by next-generation sequencing were assessed in n = 75 healthy volunteers and the genetic information comprised 278 loci. Gaussian mixture modeling indicated 2 phenotype groups with high or low capsaicin-induced hypersensitization to heat. Unsupervised machine learning implemented as swarm-based clustering hinted at differences in the genetic pattern between these phenotype groups. Several methods of supervised machine learning implemented as random forests, adaptive boosting, k-nearest neighbors, naive Bayes, support vector machines, and for comparison, binary logistic regression predicted the phenotype group association consistently better when based on the observed genotypes than when using a random permutation of the exomic sequences. Of note, TRPA1 variants were more important for correct phenotype group association than TRPV1 variants. This indicates a role of the TRPA1 and TRPV1 next-generation sequencing-based genetic pattern in the modulation of the individual response to heat-related pain phenotypes. When considering earlier evidence that topical capsaicin can induce neuropathy-like quantitative sensory testing patterns in healthy subjects, implications for future analgesic treatments with transient receptor potential inhibitors arise.

Use of Computational Functional Genomics in Drug Discovery and Repurposing for Analgesic Indications.

The novel research area of functional genomics investigates biochemical, cellular, or physiological properties of gene products with the goal of understanding the relationship between the genome and the phenotype. These developments have made analgesic drug research a data-rich discipline mastered only by making use of parallel developments in computer science, including the establishment of knowledge bases, mining methods for big data, machine-learning, and artificial intelligence, (Table ) which will be exemplarily introduced in the following.

Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective.

Genes causally involved in human insensitivity to pain provide a unique molecular source of studying the pathophysiology of pain and the development of novel analgesic drugs. The increasing availability of "big data" enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about n = 20 genes causally involved in human hereditary insensitivity to pain with the knowledge about the functions of thousands of genes. An integrated computational analysis proposed that among the functions of this set of genes, the processes related to nervous system development and to ceramide and sphingosine signaling pathways are particularly important. This is in line with earlier suggestions to use these pathways as therapeutic target in pain. Following identification of the biological processes characterizing hereditary insensitivity to pain, the biological processes were used for a similarity analysis with the functions of n = 4,834 database-queried drugs. Using emergent self-organizing maps, a cluster of n = 22 drugs was identified sharing important functional features with hereditary insensitivity to pain. Several members of this cluster had been implicated in pain in preclinical experiments. Thus, the present concept of machine-learned knowledge discovery for pain research provides biologically plausible results and seems to be suitable for drug discovery by identifying a narrow choice of repurposing candidates, demonstrating that contemporary machine-learned methods offer innovative approaches to knowledge discovery from available evidence.

Next-generation sequencing of the human TRPV1 gene and the regulating co-players LTB4R and LTB4R2 based on a custom AmpliSeq™ panel.

BACKGROUND: Transient receptor potential cation channel subfamily V member 1 (TRPV1) are sensitive to heat, capsaicin, pungent chemicals and other noxious stimuli. They play important roles in the pain pathway where in concert with proinflammatory factors such as leukotrienes they mediate sensitization and hyperalgesia. TRPV1 is the target of several novel analgesics drugs under development and therefore, TRPV1 genetic variants might represent promising candidates for pharmacogenetic modulators of drug effects. METHODS: A next-generation sequencing (NGS) panel was created for the human TRPV1 gene and in addition, for the leukotriene receptors BLT1 and BLT2 recently described to modulate TRPV1 mediated sensitisation processes rendering the coding genes LTB4R and LTB4R2 important co-players in pharmacogenetic approaches involving TRPV1. The NGS workflow was based on a custom AmpliSeq™ panel and designed for sequencing of human genes on an Ion PGM™ Sequencer. A cohort of 80 healthy subjects of Western European descent was screened to evaluate and validate the detection of exomic sequences of the coding genes with 25 base pair exon padding. RESULTS: The amplicons covered approximately 97% of the target sequence. A median of 2.81 x 106 reads per run was obtained. This identified approximately 140 chromosome loci where nucleotides deviated from the reference sequence GRCh37 hg19 comprising the three genes TRPV1, LTB4R and LTB4R2. Correspondence between NGS and Sanger derived nucleotide sequences was 100%. CONCLUSIONS: Results suggested that the NGS approach based on AmpliSeq™ libraries and Ion Personal Genome Machine (PGM) sequencing is a highly efficient mutation detection method. It is suitable for large-scale sequencing of TRPV1 and functionally related genes. The method adds a large amount of genetic information as a basis for complete analysis of TRPV1 ion channel genetics and its functional consequences.

Next-generation sequencing of human opioid receptor genes based on a custom AmpliSeq™ library and ion torrent personal genome machine.

BACKGROUND: The opioid system is involved in the control of pain, reward, addictive behaviors and vegetative effects. Opioids exert their pharmacological actions through the agonistic binding at opioid receptors and variation in the coding genes has been found to modulate opioid receptor expression or signaling. However, a limited selection of functional opioid receptor variants is perceived as insufficient in providing a genetic diagnosis of clinical phenotypes and therefore, unrestricted access to opioid receptor genetics is required. METHODS: Next-generation sequencing (NGS) workflow was based on a custom AmpliSeq™ panel and designed for sequencing of human genes related to the opioid receptor group (OPRM1, OPRD1, OPRK1, SIGMA1, OPRL1) on an Ion PGM™ Sequencer. A cohort of 79 previously studied chronic pain patients was screened to evaluate and validate the detection of exomic sequences of the coding genes with 25 base pair exon padding. In-silico analysis was performed using SNP and Variation Suite® software. RESULTS: The amplicons covered approximately 90% of the target sequence. A median of 2.54×106 reads per run was obtained generating a total of 35,447 nucleotide reads from each DNA sample. This identified approximately 100 chromosome loci where nucleotides deviated from the reference sequence GRCh37 hg19, including functional variants such as the OPRM1 rs1799971 SNP (118 A>G) as the most scientifically regarded variant or rs563649 SNP coding for µ-opioid receptor splice variants. Correspondence between NGS and Sanger derived nucleotide sequences was 100%. CONCLUSION: Results suggested that the NGS approach based on AmpliSeq™ libraries and Ion PGM sequencing is a highly efficient mutation detection method. It is suitable for large-scale sequencing of opioid receptor genes. The method includes the variants studied so far for functional associations and adds a large amount of genetic information as a basis for complete analysis of human opioid receptor genetics and its functional consequences.

A data science approach to candidate gene selection of pain regarded as a process of learning and neural plasticity.

The increasing availability of "big data" enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about n = 535 genes identified empirically as relevant to pain with the knowledge about the functions of thousands of genes. Starting from an accepted description of chronic pain as displaying systemic features described by the terms "learning" and "neuronal plasticity," a functional genomics analysis proposed that among the functions of the 535 "pain genes," the biological processes "learning or memory" (P = 8.6 × 10) and "nervous system development" (P = 2.4 × 10) are statistically significantly overrepresented as compared with the annotations to these processes expected by chance. After establishing that the hypothesized biological processes were among important functional genomics features of pain, a subset of n = 34 pain genes were found to be annotated with both Gene Ontology terms. Published empirical evidence supporting their involvement in chronic pain was identified for almost all these genes, including 1 gene identified in March 2016 as being involved in pain. By contrast, such evidence was virtually absent in a randomly selected set of 34 other human genes. Hence, the present computational functional genomics-based method can be used for candidate gene selection, providing an alternative to established methods.