Pharmacogenomics of drug efficacy in the interferon treatment of chronic hepatitis C using classification algorithms

Chronic hepatitis C (CHC) patients often stop pursuing interferon-alfa and ribavirin (IFN-alfa/RBV) treatment because of the high cost and associated adverse effects. It is highly desirable, both clinically and economically, to establish tools to distinguish responders from nonresponders and to predict possible outcomes of the IFN-alfa/RBV treatments. Single nucleotide polymorphisms (SNPs) can be used to understand the relationship between genetic inheritance and IFN-alfa/RBV therapeutic response. The aim in this study was to establish a predictive model based on a pharmacogenomic approach. Our study population comprised Taiwanese patients with CHC who were recruited from multiple sites in Taiwan. The genotyping data was generated in the high-throughput genomics lab of Vita Genomics, Inc. With the wrapper-based feature selection approach, we employed multilayer feedforward neural network (MFNN) and logistic regression as a basis for comparisons. Our data revealed that the MFNN models were superior to the logistic regression model. The MFNN approach provides an efficient way to develop a tool for distinguishing responders from nonresponders prior to treatments. Our preliminary results demonstrated that the MFNN algorithm is effective for deriving models for pharmacogenomics studies and for providing the link from clinical factors such as SNPs to the responsiveness of IFN-alfa/RBV in clinical association studies in pharmacogenomics

A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data

Background: In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for the association studies of disease susceptibility. In this work, our goal was to compare computational tools with and without feature selection for predicting chronic fatigue syndrome (CFS) using genetic factors such as single nucleotide polymorphisms ( SNPs). Methods: We employed the dataset that was original to the previous study by the CDC Chronic Fatigue Syndrome Research Group. To uncover relationships between CFS and SNPs, we applied three classification algorithms including naive Bayes, the support vector machine algorithm, and the C4.5 decision tree algorithm. Furthermore, we utilized feature selection methods to identify a subset of influential SNPs. One was the hybrid feature selection approach combining the chi-squared and information-gain methods. The other was the wrapper- based feature selection method. Results: The naive Bayes model with the wrapper-based approach performed maximally among predictive models to infer the disease susceptibility dealing with the complex relationship between CFS and SNPs. Conclusion: We demonstrated that our approach is a promising method to assess the associations between CFS and SNPs

Support vector machine for simultaneous determination of ultra trace concentrations of copper and cadmium in serum of patients with chronic hepatitis by adsorptive stripping voltammetry

The purpose of this study is to establish a simple model and use the clinical data to predict the interferon efficacy. This model is a combination of Feature Subset Selection and the Classifier using a Support Vector Machine (SVM). The study indicates that when five features have been selected, the identification by the SVM is as follows: the identification rate for the effective group is 85%, and the ineffective group 83%.Serum trace elements concentrations and their ratios are frequently reported to be a good marker for diagnosing various diseases; one of these diseases is chronic liver diseases.  This study undertaken to simultaneous determine of Cu2+, and Cd2+ concentrations in sera from viral hepatitis (B and C) patients by adsorptive stripping voltammetry using 3-aminophthalhydrazide (luminol) as a complex agent and classification with support vector machine SVM. Donor's hepatitis patients and healthy persons were selected from different environmental areas, including Arar, Al-auiqilh and Rafha as unpolluted areas, and Al-Jouf , Tarif and Al- Qurayyat as a polluted areas. Hepatitis patients from polluted areas exhibited higher Cu2+ and Cd2+concentrations in their serum than those from the other areas. Patients with hepatitis B show higher levels of Cu2+, and Cd2+ in their serum than those with hepatitis C. Copper and cadmium presented at higher level in patient serum than in healthy ones. In this study the optimum reaction parameters and conditions studies are investigated. The calibration graphs were linear in the concentration range of 0.3– 142.5 and 0.065–60.0 ng/mL for copper and cadmium, respectively. The limit of detection of the method was 0.038ng/mL for Cu2+ and 0.013 ng/mL for Cd2+. The interference of some common ions was studied and it was concluded that application of this method for the determination of Cu2+ and Cd2+in the healthy control and hepatitis patient's serum led to satisfactory results. Keywords: Support Vector Machine; adsorptive stripping voltammetry; chronic hepatitis; heavy metal

Machine learning techniques for personalised medicine approaches in immune-mediated chronic inflammatory diseases: Applications and challenges

In the past decade, the emergence of machine learning (ML) applications has led to significant advances towards implementation of personalised medicine approaches for improved health care, due to the exceptional performance of ML models when utilising complex big data. The immune-mediated chronic inflammatory diseases are a group of complex disorders associated with dysregulated immune responses resulting in inflammation affecting various organs and systems. The heterogeneous nature of these diseases poses great challenges for tailored disease management and addressing unmet patient needs. Applying novel ML techniques to the clinical study of chronic inflammatory diseases shows promising results and great potential for precision medicine applications in clinical research and practice. In this review, we highlight the clinical applications of various ML techniques for prediction, diagnosis and prognosis of autoimmune rheumatic diseases, inflammatory bowel disease, autoimmune chronic kidney disease, and multiple sclerosis, as well as ML applications for patient stratification and treatment selection. We highlight the use of ML in drug development, including target identification, validation and drug repurposing, as well as challenges related to data interpretation and validation, and ethical concerns related to the use of artificial intelligence in clinical research

Computational approaches for improving treatment and prevention of viral infections

The treatment of infections with HIV or HCV is challenging. Thus, novel drugs and new computational approaches that support the selection of therapies are required. This work presents methods that support therapy selection as well as methods that advance novel antiviral treatments. geno2pheno[ngs-freq] identifies drug resistance from HIV-1 or HCV samples that were subjected to next-generation sequencing by interpreting their sequences either via support vector machines or a rules-based approach. geno2pheno[coreceptor-hiv2] determines the coreceptor that is used for viral cell entry by analyzing a segment of the HIV-2 surface protein with a support vector machine. openPrimeR is capable of finding optimal combinations of primers for multiplex polymerase chain reaction by solving a set cover problem and accessing a new logistic regression model for determining amplification events arising from polymerase chain reaction. geno2pheno[ngs-freq] and geno2pheno[coreceptor-hiv2] enable the personalization of antiviral treatments and support clinical decision making. The application of openPrimeR on human immunoglobulin sequences has resulted in novel primer sets that improve the isolation of broadly neutralizing antibodies against HIV-1. The methods that were developed in this work thus constitute important contributions towards improving the prevention and treatment of viral infectious diseases.Die Behandlung von HIV- oder HCV-Infektionen ist herausfordernd. Daher werden neue Wirkstoffe, sowie neue computerbasierte Verfahren benötigt, welche die Therapie verbessern. In dieser Arbeit wurden Methoden zur Unterstützung der Therapieauswahl entwickelt, aber auch solche, welche neuartige Therapien vorantreiben. geno2pheno[ngs-freq] bestimmt, ob Resistenzen gegen Medikamente vorliegen, indem es Hochdurchsatzsequenzierungsdaten von HIV-1 oder HCV Proben mittels Support Vector Machines oder einem regelbasierten Ansatz interpretiert. geno2pheno[coreceptor-hiv2] bestimmt den HIV-2 Korezeptorgebrauch dadurch, dass es einen Abschnitt des viralen Oberflächenproteins mit einer Support Vector Machine analysiert. openPrimeR kann optimale Kombinationen von Primern für die Multiplex-Polymerasekettenreaktion finden, indem es ein Mengenüberdeckungsproblem löst und auf ein neues logistisches Regressionsmodell für die Vorhersage von Amplifizierungsereignissen zurückgreift. geno2pheno[ngs-freq] und geno2pheno[coreceptor-hiv2] ermöglichen die Personalisierung antiviraler Therapien und unterstützen die klinische Entscheidungsfindung. Durch den Einsatz von openPrimeR auf humanen Immunoglobulinsequenzen konnten Primersätze generiert werden, welche die Isolierung von breit neutralisierenden Antikörpern gegen HIV-1 verbessern. Die in dieser Arbeit entwickelten Methoden leisten somit einen wichtigen Beitrag zur Verbesserung der Prävention und Therapie viraler Infektionskrankheiten

Interferon signaling in chronic hepatitis C : mechanisms and implications for therapy

Hepatitis C virus (HCV) infection is a major cause of chronic liver disease worldwide and can lead to liver cirrhosis and hepatocellular carcinoma. The current standard therapy of chronic hepatitis C (CHC) consists of a combination of pegylated interferon alpha (pegIFNα) and ribavirin. However, sustained viral clearance is achieved in only 50-60% of patients. The underlying mechanism of failure of pegIFNα based therapy remains unknown and no molecular or genetic markers have been identified that could predict the treatment outcome. The overall aim of the study described in this thesis is to understand the molecular basis for failure of IFNα based therapies in patients with CHC. The study has focused on the IFNinduced Jak-STAT (janus kinase-signal transducer and activator of transcription) signaling pathway. To address the molecular basis of treatment response to IFN therapy, three experimental approaches have been employed. The first approach involved the analysis of IFNα signaling and expression of interferon stimulated genes (ISGs) in liver biopsies and peripheral blood mononuclear cells (PBMCs) of HCV patients undergoing pegIFNα treatment. Paired liver biopsies and PBMCs from 16 patients were collected before andhours after the first injection of pegIFNα, and were subjected to analysis of global gene expression using Affymetrix arrays. Further, activation of the IFN-induced Jak-STAT signaling pathway was analyzed by immunoblotting, immunohistochemistry and gel shift assays. The correlation of these biochemical and molecular data with the clinical response to treatment demonstrated that in the liver of patients with a rapid response pegIFNα induced a strong upregulation of ISGs, whereas in patients that did not respond to therapy, induction of IFN-dependent gene expression was impaired. Surprisingly, the non-responders had maximally induced ISG expression already before treatment with pegIFNα. Furthermore, the analyses of STAT1 phosphorylation, nuclear localization and DNA binding confirmed that the endogenous IFN signaling pathway in non-responders is pre-activated and refractory to further stimulation. In contrast to liver samples, ISG expression in PBMCs was stimulated by pegIFNα in both responders and nonresponders, indicating that PBMCs are not a good surrogate marker for IFNα responses in the liver and that chronic HCV infection has strong local effects on the IFN system in liver. Our findings support an interesting concept that activation of the endogenous IFN system in CHC not only is ineffective in clearing the infection, but may also impede the response to therapy, most likely by inducing a refractory state of the IFN signaling pathway in the liver. In the second approach we addressed the mechanisms underlying the pre-activation of the endogenous IFN system in a defined group of HCV patients (future non-responders). For this purpose, we analyzed ISG expression by quantitative RT-PCR and nuclear localization of STAT1 by immunohistochemistry in a cohort of 112 patients with CHC. By subdividing this cohort according to the HCV genotype (GT), we discovered that patients infected with HCV GT 1 and 4 more often show hepatic ISG preactivation than GT 2 and 3 patients, thus providing an explanation for the poor response to IFN therapy seen in GT 1/4 patients. We analyzed the possible involvement of viral sensory pathways in type I IFN production and ISG upregulation. Previously, the viral HCV NS3-4A protease was shown to interfere with viral sensory pathways by cleaving and thereby inactivating an important adaptor molecule, Cardif. We therefore assessed Cardif cleavage in liver biopsies of HCV patients and found that cleavage more often occurred in patients infected with HCV GTs 2 and 3. Our findings support a concept that the success of the virus in preventing the induction of the endogenous IFN system in the livers of these patients would, however, come at the cost of being more susceptible to IFNα therapies as is the case with GT 2/3 patients. In the third approach we designed an experimental model to study the molecular basis of refractoriness of IFN signaling in vivo. Previously, cell culture experiments demonstrated a long lasting desensitization period, which followed the initial activation of the IFNα signaling pathway. In the approach used here, we established a mouse model in which continuous presence of IFNα in vivo was achieved by multiple subcutaneous injections, mimicking the constitutively high serum levels achieved by pegIFNα in patients. Interestingly, this resulted in refractoriness of IFNα signaling. Activation of STAT1 and STAT2, but not STAT3, in the mouse liver was desensitized by continuous IFNα stimulation. To elucidate the mechanism of this refractoriness, the role of negative regulators of the Jak- STAT signaling pathway was investigated. IFN signaling remained refractory in mice deficient in suppressor of cytokine signaling (SOCS) 3 and persisting refractoriness was also observed in mice deficient in IL-10, a strong inducer of SOCS3. Ubiquitin specific peptidase 18 (USP18/UBP43) was recently identified as novel negative regulator of IFNα signal transduction. Interestingly, refractoriness could be overcome in USP18/UBP43 knockout mice. These data strongly indicate that UBP43 is the decisive factor in inducing a refractory state in the IFNα signaling pathway in vivo

Modelling the genomic structure, and antiviral susceptibility of Human Cytomegalovirus

Human Cytomegalovirus (HCMV) is found ubiquitously in humans worldwide, and once acquired, the infection persists within the host throughout their life. Although Immunocompetent people rarely are affected by HCMV infections, their related diseases pose a major health problem worldwide for those with compromised or suppressed immune systems such as transplant recipients. Additionally, congenital transmission of HCMV is the most common infectious cause of birth defects globally and is associated with a substantial economic burden. This thesis explores the application of statistical modelling and genomics to unpick three key areas of interest in HCMV research. First, a comparative genomics analysis of global HCMV strains was undertaken to delineate the molecular population structure of this highly variable virus. By including in-house sequenced viruses of African origin and by developing a statistical framework to deconvolute highly variable regions of the genome, novel and important insights into the co-evolution of HCMV with its host were uncovered. Second, a rich database relating mutations to drug sensitivity was curated for all the antiviral treated herpesviruses. This structured information along with the development of a mutation annotation pipeline, allowed the further development of statistical models that predict the phenotype of a virus from its sequence. The predictive power of these models was validated for HSV1 by using external unseen mutation data provided in collaboration with the UK Health Security Agency. Finally, a nonlinear mixed effects model, expanded to account for Ganciclovir pharmacokinetics and pharmacodynamics, was developed by making use of rich temporal HCMV viral load data. This model allowed the estimation of the impact of immune-clearance versus antiviral inhibition in controlling HCMV lytic replication in already established infections post-haematopoietic stem cell transplant

Determining and utilizing the quasispecies of the hepatitis B virus in clinical applications

Chronic hepatitis B caused by infection with the hepatitis B virus (HBV) affects about 240 million people worldwide and is one of the major causes of severe liver cirrhosis and liver cancer. Hepatitis B treatment options have improved dramatically in the last decade. Effective direct-acting antiviral drugs, so-called nucleos(t)ide analogs, and one effective immunomodulatory drug (pegylated interferon alpha-2a) are available presently. Current challenges for treating HBV involve the careful selection of patients who require therapy and the thoughtful choice of the treatment option tailored to each patient individually. Personalized medicine aims to optimize treatment decisions based on the analysis of host factors and virus characteristics. The population of viruses within a host is called the viral quasispecies. This thesis provides statistical methods to infer relevant information about the viral quasispecies of HBV to support treatment decisions. We introduce a new genotyping methodology to identify dual infections, which can help to quantify the risk of interferon therapy failure. We present a method to infer short-range linkage information from Sanger sequencing chromatograms, a method to support treatment adjustment after the development of resistance to nucleos(t)ide analogs. Additionally, we provide the first full-genome analysis of the G-to-A hypermutation patterns of the HBV genome. Hypermutated viral genomes form a subpopulation of the quasispecies caused by proteins of the human innate immune system editing the genome of exogenous viral agents. We show that hypermutation is associated with the natural progression of hepatitis B, but does not correlate with treatment response to interferon.Die Hepatitis-B-Erkrankung wird durch eine Infektion mit dem Hepatitis-B-Virus (HBV) verursacht. Weltweit sind schätzungsweise 240 Millionen Menschen chronisch infiziert. Dabei stellt Hepatitis-B eine der häufigsten Ursachen für die Entwicklung von Leberzirrhose und Leberkrebs dar. Die Behandlungsmöglichkeiten wurden in den letzten zehn Jahren signifikant verbessert. Mittlerweile stehen effektive direkt antivirale Medikamente – sogenannte Nukleos(t)id-Analoga – und ein effektives immunmodulierendes Medikament (pegyliertes Interferon alpha-2a) für die Behandlung zur Verfügung. Zentrale Fragen bei der Behandlung von Hepatitis-B beinhalten die zielgerichtete Auswahl der Patienten, welche therapiert werden müssen, sowie die passgenaue Auswahl der Behandlungsoption. Die personalisierte Medizin verfolgt das Ziel, die Behandlung basierend auf der Analyse von Patientencharakteristika und Eigenschaften des Virus zu optimieren. Die Gesamtheit der Viren innerhalb eines Wirtes wird als virale Quasispezies bezeichnet. Diese Arbeit stellt statistische Methoden zur Verfügung, um relevante Informationen über die Quasispezies von HBV zur Unterstützung von Therapieentscheidungen zu ermitteln. Wir entwickeln eine neue Methode zur Genotypisierung, welche Zweifachinfektionen mit HBV identifiziert und somit hilfreich sein kann, das Risiko eines Therapieversagens einer Interferonbehandlung korrekt einzuschätzen. Des Weiteren stellen wir eine Methode vor, welche Linkage-Informationen der viralen Quasispezies, basierend auf den Chromatogrammen der DNA-Sequenzierung nach Sanger, extrahieren kann. Diese Methode kann bei der Umstellung einer Therapie mit Nukleos(t)id-Analoga nach Resistenzentwicklung verwendet werden. Schließlich präsentieren wir die erste Vollgenomanalyse der G-zu-A Hypermutationsmuster von HBV. Hypermutierte virale Genome stellen eine Teilmenge der Quasispezies dar, welche durch von Proteinen der angeborenen Immunabwehr bewirkte Mutationen im viralen Genom entsteht. Wir zeigen, dass diese Subpopulation mit dem natürlichen Verlauf einer Hepatitis-B-Erkrankung, jedoch nicht mit dem Therapieansprechen auf Interferon, statistisch signifikant assoziiert werden kann