Application of genomics, proteomics and metabolomics in drug discovery, development and clinic

Genomics, proteomics and metabolomics are three areas that are routinely applied throughout the drug-development process as well as after a product enters the market. This review discusses all three 'omics, reporting on the key applications, techniques, recent advances and expectations of each. Genomics, mainly through the use of novel and next-generation sequencing techniques, has advanced areas of drug discovery and development through the comparative assessment of normal and diseased-state tissues, transcription and/or expression profiling, side-effect profiling, pharmacogenomics and the identification of biomarkers. Proteomics, through techniques including isotope coded affinity tags, stable isotopic labeling by amino acids in cell culture, isobaric tags for relative and absolute quantification, multidirectional protein identification technology, activity-based probes, protein/peptide arrays, phage displays and two-hybrid systems is utilized in multiple areas through the drug development pipeline including target and lead identification, compound optimization, throughout the clinical trials process and after market analysis. Metabolomics, although the most recent and least developed of the three 'omics considered in this review, provides a significant contribution to drug development through systems biology approaches. Already implemented to some degree in the drug-discovery industry and used in applications spanning target identification through to toxicological analysis, metabolic network understanding is essential in generating future discoveries.

Application of genomics, proteomics and metabolomics in drug discovery, development and clinic

Genomics, proteomics and metabolomics are three areas that are routinely applied throughout the drug-development process as well as after a product enters the market. This review discusses all three 'omics, reporting on the key applications, techniques, recent advances and expectations of each. Genomics, mainly through the use of novel and next-generation sequencing techniques, has advanced areas of drug discovery and development through the comparative assessment of normal and diseased-state tissues, transcription and/or expression profiling, side-effect profiling, pharmacogenomics and the identification of biomarkers. Proteomics, through techniques including isotope coded affinity tags, stable isotopic labeling by amino acids in cell culture, isobaric tags for relative and absolute quantification, multidirectional protein identification technology, activity-based probes, protein/peptide arrays, phage displays and two-hybrid systems is utilized in multiple areas through the drug development pipeline including target and lead identification, compound optimization, throughout the clinical trials process and after market analysis. Metabolomics, although the most recent and least developed of the three 'omics considered in this review, provides a significant contribution to drug development through systems biology approaches. Already implemented to some degree in the drug-discovery industry and used in applications spanning target identification through to toxicological analysis, metabolic network understanding is essential in generating future discoveries.

Using secondary knowledge to support decision tree classification of retrospective clinical data

Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and reasoning in any explicit manner. In this research we describe applying data mining methods to retrospective clinical data to build a prediction model for asthma exacerbation severity for pediatric patients in the emergency department. Difficulties in building such a model forced us to investigate alternative strategies for analyzing and processing retrospective data. This paper describes this process together with an approach to mining retrospective clinical data by incorporating formalized external expert knowledge (secondary knowledge sources) into the classification task. This knowledge is used to partition the data into a number of coherent sets, where each set is explicitly described in terms of the secondary knowledge source. Instances from each set are then classified in a manner appropriate for the characteristics of the particular set. We present our methodology and outline a set of experiential results that demonstrate some advantages and some limitations of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

A method for automatically eliciting node weights in a hierarchical knowledge-based structure for reasoning with uncertainty

Hierarchical knowledge structures are frequently used within clinical decision support systems as part of the model for generating intelligent advice. The nodes in the hierarchy inevitably have varying influence on the decisionmaking processes, which needs to be reflected by parameters. If the model has been elicited from human experts, it is not feasible to ask them to estimate the parameters because there will be so many in even moderately-sized structures. This paper describes how the parameters could be obtained from data instead, using only a small number of cases. The original method [1] is applied to a particular web-based clinical decision support system called GRiST, which uses its hierarchical knowledge to quantify the risks associated with mental-health problems. The knowledge was elicited from multidisciplinary mental-health practitioners but the tree has several thousand nodes, all requiring an estimation of their relative influence on the assessment process. The method described in the paper shows how they can be obtained from about 200 cases instead. It greatly reduces the experts’ elicitation tasks and has the potential for being generalised to similar knowledge-engineering domains where relative weightings of node siblings are part of the parameter space.

An evaluation of the epidemiology of medication discrepancies and clinical significance of medicines reconciliation in children admitted to hospital

Aims: To determine the incidence of unintended medication discrepancies in paediatric patients at the time of hospital admission; evaluate the process of medicines reconciliation; assess the benefit of medicines reconciliation in preventing clinical harm. Method: A 5 month prospective multisite study. Pharmacists at four English hospitals conducted admission medicines reconciliation in children using a standardised data collection form. A discrepancy was defined as a difference between the patient's preadmission medication (PAM), compared with the initial admission medication orders written by the hospital doctor. The discrepancies were classified into intentional and unintentional discrepancies. The unintentional discrepancies were assessed for potential clinical harm by a team of healthcare professionals, which included doctors, pharmacists and nurses. Results: Medicines reconciliation was conducted in 244 children admitted to hospital. 45% (109/244) of the children had at least one unintentional medication discrepancy between the PAM and admission medication order. The overall results indicated that 32% (78/244) of patients had at least one clinically significant unintentional medication discrepancy with potential to cause moderate 20% (50/244) or severe 11% (28/244) harm. No single source of information provided all the relevant details of a patient's medication history. Parents/carers provided the most accurate details of a patient's medication history in 81% of cases. Conclusions: This study demonstrates that in the absence of medicines reconciliation, children admitted to hospitals across England are at risk of harm from unintended medication discrepancies at the transition of care from the community to hospital. No single source of information provided a reliable medication history.

Managing healthcare quality using combined SWOT and the analytic process

Improving healthcare quality is a growing need of any society. Although various quality improvement projects are routinely deployed by the healthcare professional, they are characterised by a fragmented approach, i.e. they are not linked with the strategic intent of the organisation. This study introduces a framework which integrates all quality improvement projects with the strategic intent of the organisation. It first derives the strengths, weaknesses, opportunities and threats (SWOT) matrix of the system with the involvement of the concerned stakeholders (clinical professional), which helps identify a few projects, the implementation of which ensures achievement of desired quality. The projects are then prioritised using the analytic hierarchy process with the involvement of the concerned stakeholders (clinical professionals) and implemented in order to improve system performance. The effectiveness of the method has been demonstrated using a case study in the intensive care unit of Queen Elizabeth Hospital in Bridgetown, Barbados.

Ocular compatibility of hydrogel contact lenses:deposition and clinical performance

Currently over 50 million people worldwide wear contact lenses, of which over 75% wear hydrogel lenses. Significant deposition occurs in approximately 80% of hydrogel lenses and many contact lens wearers cease wearing lenses due to problems associated with deposition. The contact lens field is not alone in encountering complications associated with interactions between the body and artificial devices. The widespread use of man-made materials to replace structures in the body has emphasised the importance of studies that examine the interactions between implantation materials and body tissues.This project used carefully controlled, randomized clinical studies to study the interactive effects of contact lens materials, care systems, replacement periods and patient differences. Of principal interest was the influence of these factors on material deposition and their subsequent impact on subjective performance. A range of novel and established analytical techniques were used to examine hydrogel lenses following carefully controlled clinical studies in which clinical performance was meticulously monitored. These studies established the inter-relationship between clinical performance and deposition to be evaluated. This project showed that significant differences exist between individuals in their ability to deposit hydrogel lenses, with approximately 20% of subjects displaying significant deposition irrespective of the lens material. Additionally, materials traditionally categorised together show markedly different spoilation characteristics, which are wholly attributable to their detailed chemical structure. For the first time the in vivo deposition kinetics of both protein and lipid in charged and uncharged polymers was demonstrated. In addition the importance of care systems in the deposition process was shown, clearly demonstrating the significance of the quality rather than the quantity of deposition in influencing subjective performance.

The production of clinical dextran using membrane processes

Clinical dextran is used as a blood volume expander. The British Pharmacopeia (BP) specification for this product requires the amount of dextran below 12,000 MW and above 98,000 MW to be strictly controlled. Dextran is presently fractionated industrially using ethanol precipitation. The aim of this work was to develop an ultrafiltration system which could replace the present industrial process. Initially these molecular weight (MW) bands were removed using batch ultrafiltration. A large number of membranes were tested. The correct BP specification could be achieved using these membranes but there was a significant loss of saleable material. To overcome this problem a four stage ultrafiltration cascade (UFC) was used. This work is the first known example of a UFC being used to remove both the high and low MW dextran. To remove the high MW material it was necessary to remove 90% of the MW distribution and retain the remaining 10%. The UFC significantly reduced the amount of dialysate required. To achieve the correct specification below 12,000 MW, the UFC required only 2.5 - 3.0 diavolumes while the batch system required 6 - 7. The UFC also improved the efficiency of the fractionation process. The UFC could retain up to 96% of the high MW material while the batch system could only retain 82.5% using the same number of diavolumes. On average the UFC efficiency was approximately 10% better than the equivalent batch system. The UFC was found to be more predictable than the industrial process and the specification of the final product was easier to control. The UFC can be used to improve the fractionation of any polymer and also has several other potential uses including enzyme purification. A dextransucrase bioreactor was also developed. This preliminary investigation highlighted the problems involved with the development of a successful bioreactor for this enzyme system.

The fractionation of dextran polymer by ultrafiltration to yield clinical products

A review of ultrafiltration (UF) theory and equipment has been made. Dextran is fractionated industrially by ethanol precipitation, which is a high energy intensive process. The aims of this work were to investigate the fractionation of dextran using UF and to compare the efficiency and costs of UF fractionation with ethanol fractionation. This work is the continuation of research conducted at Aston, which was concerned with the fractionation of dextran using gel permeation chromatography (GPC) and hollow fibre UF membranes supplied by Amicon Ltd. Initial laboratory work centred on determining the most efficient make and configuration of membrane. UF membranes of the Millipore cassette configuration, and the DDS flat-sheet configuration, were examined for the fracationation of low molecular weight (MW) dextran. When compared to Amicon membranes, these membranes were found to be inferior. DDS membranes of 25 000 and 50 000 MW cut-offs were shown to be capable of fractionating high MW dextran with the same efficiency as GPC. The Amicon membranes had an efficiency comparable to that of ethanol fractionation. To increase this efficiency a theoretical UF membrane cascade was adopted to utilize favourable characteristics encountered in batch mode membrane experiments. The four stage cascade used recycled permeates in a counter- current direction to retentate flow, and was operated 24 hours per day controlled by a computer. Using 5 000 MW cut-off membranes the cascade improved the batch efficiency by at least 10% for a fractionation at 6 000 MW. Economic comparisons of ethanol fractionation, combined GPC and UF fractionation, and UF fractionation of dextran were undertaken. On an economic basis GPC was the best method for high MW dextran fractionation. When compared with a plant producing 100 tonnes pa of clinical dextran, by ethanol fractionation, a combined GPC and UF cascade fractionation could produce savings on operating costs and an increased dextran yield of 5%.

Predicting and diagnosing faults in wastewater treatment process by Bayesian Belief Networks

Diagnosing faults in wastewater treatment, like diagnosis of most problems, requires bi-directional plausible reasoning. This means that both predictive (from causes to symptoms) and diagnostic (from symptoms to causes) inferences have to be made, depending on the evidence available, in reasoning for the final diagnosis. The use of computer technology for the purpose of diagnosing faults in the wastewater process has been explored, and a rule-based expert system was initiated. It was found that such an approach has serious limitations in its ability to reason bi-directionally, which makes it unsuitable for diagnosing tasks under the conditions of uncertainty. The probabilistic approach known as Bayesian Belief Networks (BBNS) was then critically reviewed, and was found to be well-suited for diagnosis under uncertainty. The theory and application of BBNs are outlined. A full-scale BBN for the diagnosis of faults in a wastewater treatment plant based on the activated sludge system has been developed in this research. Results from the BBN show good agreement with the predictions of wastewater experts. It can be concluded that the BBNs are far superior to rule-based systems based on certainty factors in their ability to diagnose faults and predict systems in complex operating systems having inherently uncertain behaviour.

Knowledge engineering for mental-health risk assessment and decision support

Mental-health risk assessment practice in the UK is mainly paper-based, with little standardisation in the tools that are used across the Services. The tools that are available tend to rely on minimal sets of items and unsophisticated scoring methods to identify at-risk individuals. This means the reasoning by which an outcome has been determined remains uncertain. Consequently, there is little provision for: including the patient as an active party in the assessment process, identifying underlying causes of risk, and eecting shared decision-making. This thesis develops a tool-chain for the formulation and deployment of a computerised clinical decision support system for mental-health risk assessment. The resultant tool, GRiST, will be based on consensual domain expert knowledge that will be validated as part of the research, and will incorporate a proven psychological model of classication for risk computation. GRiST will have an ambitious remit of being a platform that can be used over the Internet, by both the clinician and the layperson, in multiple settings, and in the assessment of patients with varying demographics. Flexibility will therefore be a guiding principle in the development of the platform, to the extent that GRiST will present an assessment environment that is tailored to the circumstances in which it nds itself. XML and XSLT will be the key technologies that help deliver this exibility.

An investigation into inductive parameter learning in complex hierarchical knowledge structures representing clinical expertise

This dissertation investigates the very important and current problem of modelling human expertise. This is an apparent issue in any computer system emulating human decision making. It is prominent in Clinical Decision Support Systems (CDSS) due to the complexity of the induction process and the vast number of parameters in most cases. Other issues such as human error and missing or incomplete data present further challenges. In this thesis, the Galatean Risk Screening Tool (GRiST) is used as an example of modelling clinical expertise and parameter elicitation. The tool is a mental health clinical record management system with a top layer of decision support capabilities. It is currently being deployed by several NHS mental health trusts across the UK. The aim of the research is to investigate the problem of parameter elicitation by inducing them from real clinical data rather than from the human experts who provided the decision model. The induced parameters provide an insight into both the data relationships and how experts make decisions themselves. The outcomes help further understand human decision making and, in particular, help GRiST provide more accurate emulations of risk judgements. Although the algorithms and methods presented in this dissertation are applied to GRiST, they can be adopted for other human knowledge engineering domains.

Development of a probabilistic graphical structure from a model of mental health clinical expertise

This thesis explores the process of developing a principled approach for translating a model of mental-health risk expertise into a probabilistic graphical structure. Probabilistic graphical structures can be a combination of graph and probability theory that provide numerous advantages when it comes to the representation of domains involving uncertainty, domains such as the mental health domain. In this thesis the advantages that probabilistic graphical structures offer in representing such domains is built on. The Galatean Risk Screening Tool (GRiST) is a psychological model for mental health risk assessment based on fuzzy sets. In this thesis the knowledge encapsulated in the psychological model was used to develop the structure of the probability graph by exploiting the semantics of the clinical expertise. This thesis describes how a chain graph can be developed from the psychological model to provide a probabilistic evaluation of risk that complements the one generated by GRiST’s clinical expertise by the decomposing of the GRiST knowledge structure in component parts, which were in turned mapped into equivalent probabilistic graphical structures such as Bayesian Belief Nets and Markov Random Fields to produce a composite chain graph that provides a probabilistic classification of risk expertise to complement the expert clinical judgements

Designing multiple user perspectives and functionality for clinical decision support systems

Clinical Decision Support Systems (CDSSs) need to disseminate expertise in formats that suit different end users and with functionality tuned to the context of assessment. This paper reports research into a method for designing and implementing knowledge structures that facilitate the required flexibility. A psychological model of expertise is represented using a series of formally specified and linked XML trees that capture increasing elements of the model, starting with hierarchical structuring, incorporating reasoning with uncertainty, and ending with delivering the final CDSS. The method was applied to the Galatean Risk and Safety Tool, GRiST, which is a web-based clinical decision support system (www.egrist.org) for assessing mental-health risks. Results of its clinical implementation demonstrate that the method can produce a system that is able to deliver expertise targetted and formatted for specific patient groups, different clinical disciplines, and alternative assessment settings. The approach may be useful for developing other real-world systems using human expertise and is currently being applied to a logistics domain. © 2013 Polish Information Processing Society.

Form follows function:model-driven engineering for clinical trials

We argue that, for certain constrained domains, elaborate model transformation technologies-implemented from scratch in general-purpose programming languages-are unnecessary for model-driven engineering; instead, lightweight configuration of commercial off-the-shelf productivity tools suffices. In particular, in the CancerGrid project, we have been developing model-driven techniques for the generation of software tools to support clinical trials. A domain metamodel captures the community's best practice in trial design. A scientist authors a trial protocol, modelling their trial by instantiating the metamodel; customized software artifacts to support trial execution are generated automatically from the scientist's model. The metamodel is expressed as an XML Schema, in such a way that it can be instantiated by completing a form to generate a conformant XML document. The same process works at a second level for trial execution: among the artifacts generated from the protocol are models of the data to be collected, and the clinician conducting the trial instantiates such models in reporting observations-again by completing a form to create a conformant XML document, representing the data gathered during that observation. Simple standard form management tools are all that is needed. Our approach is applicable to a wide variety of information-modelling domains: not just clinical trials, but also electronic public sector computing, customer relationship management, document workflow, and so on. © 2012 Springer-Verlag.