Efficiency of antisense oligonucleotide drug discovery

The costs for discovering and developing new drugs continue to escalate, with current estimates that the average cost is more than $800 million for each new drug brought to the market. Pharmaceutical companies are under enormous pressure to increase their efficiency for bringing new drugs to the market by third-party payers, shareholders, and their patients, and at the same time regulators are placing increased demands on the industry. To be successful in the future, pharmaceutical companies must change how they discover and develop new drugs. So far, new technologies have done little to increase overall efficiency of the industry and have added additional costs. Platform technologies such as monoclonal antibodies and antisense oligonucleotides have the potential of reducing costs for discovery of new drugs, in that many of the steps required for traditional small molecules can be skipped or streamlined. Additionally the success of identifying a drug candidate is much higher with platform technologies compared to small molecule drugs. This review will highlight some of the efficiencies of antisense oligonucleotide drug discovery compared to traditional drugs and will point out some of the current limitations of the technology.     

Genomics to tree breeding and forest health

Genomic discovery in forest trees follows paradigms from both agricultural crop and livestock improvement and human medicine. Forest trees in a domesticated state can be improved using genomic-based breeding technologies, whereas the health of trees in a natural and undomesticated state might be managed using those same technologies. These applications begin by first dissecting complex traits in trees to their individual gene components and for that the association genetics approach is quite powerful in trees. This is true for several reasons including large, random mating, and unstructured populations and the rapid decay of linkage disequilibrium in many tree species. Once marker by trait associations are discovered, they can be used in genomic-based breeding and forest health diagnostics. Initial studies in trees have found ample nucleotide diversity in candidate genes to perform association studies and single nucleotide polymorphisms have been associated with economic and adaptive traits. Population genetic neutrality tests have been applied to identify genes probably under natural selection and thus make good candidates for developing forest health diagnostic tools.     

A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease

We developed a pipeline to integrate the proteomic technologies used from the discovery to the verification stages of plasma biomarker identification and applied it to identify early biomarkers of cardiac injury from the blood of patients undergoing a therapeutic, planned myocardial infarction (PMI) for treatment of hypertrophic cardiomyopathy. Sampling of blood directly from patient hearts before, during and after controlled myocardial injury ensured enrichment for candidate biomarkers and allowed patients to serve as their own biological controls. LC-MS/MS analyses detected 121 highly differentially expressed proteins, including previously credentialed markers of cardiovascular disease and >100 novel candidate biomarkers for myocardial infarction (MI). Accurate inclusion mass screening (AIMS) qualified a subset of the candidates based on highly specific, targeted detection in peripheral plasma, including some markers unlikely to have been identified without this step. Analyses of peripheral plasma from controls and patients with PMI or spontaneous MI by quantitative multiple reaction monitoring mass spectrometry or immunoassays suggest that the candidate biomarkers may be specific to MI. This study demonstrates that modern proteomic technologies, when coherently integrated, can yield novel cardiovascular biomarkers meriting further evaluation in large, heterogeneous cohorts.     

Estimating cell lineage from distributions of randomly introduced markers

Cell lineage of a multicellular organism has been analysed by introducing a genetic or chemical marker that is inherited from a cell to its daughter cells and is detectable even after several cell divisions. To construct a complete cell lineage, all the cells at different developmental stages need to be identified, and then the intracellular marker must be introduced to each cell. In this paper, I study a new method of estimating cell lineage based on distributions of intercellular markers observed at a single stage, which are introduced randomly at earlier stages. Assumptions are: (1) cell lineage is invariant between embryos; (2) a small number of cells are marked in each experiment; and (3) the total number of replicate experiments is sufficiently large. Then we identify the most likely cell lineage pattern (or tree topology) as the one that requires the least marker insertions to be compatible with the observed distributions of cell markers. This method is essentially the same as the principle of persimony widely used for ancestral phylogeny reconstruction in evolutionary biology. When the total number of cells is small, we can generate all the possible cell lineages and calculate the minimum number of marker insertions for each candidate, and then choose the cell lineage that requires the least marker insertions. If the number of cells is large, we can use clustering method in which a pair of cells with the highest correlation in marker labelling are merged sequentially. The efficiency of the clustering method in estimating the correct cell lineage is confirmed by computer simulations. Finally, the clustering method is applied to reconstruct the cell lineage of ascidian from experimental data.     

Simultaneous maximum likelihood estimation of linkage and linkage phases in outcrossing species

With the advent of new molecular marker technologies, it is now feasible to initiate genome projects for outcrossing plant species, which have not received much attention in genetic research, despite their great agricultural and environmental value. Because outcrossing species typically have heterogeneous genomes, data structure for molecular markers representing an entire genome is complex: some markers may have more alleles than others, some markers are codominant whereas others are dominant, and some markers are heterozygous in one parent but fixed in the other parent whereas the opposite can be true for other markers. A major difficulty in analyzing these different types of marker at the same time arises from uncertainty about parental linkage phases over markers. In this paper, we present a general maximum-likelihood-based algorithm for simultaneously estimating linkage and linkage phases for a mixed set of different marker types containing fully informative markers (segregating 1:1:1:1) and partially informative markers (or missing markers, segregating 1:2:1, 3:1, and 1:1) in a full-sib family derived from two outbred parent plants. The characterization of linkage phases is based on the posterior probability distribution of the assignment of alternative alleles at given markers to two homologous chromosomes of each parent, conditional on the observed phenotypes of the markers. Two- and multi-point analyses are performed to estimate the recombination fraction and determine the most likely linkage phase between different types of markers. A numerical example is presented to demonstrate the statistical properties of the model for characterizing the linkage phase between markers.     

Molecular markers of tumor initiation and progression

Of the hundreds of genes and proteins reported to be altered in human cancers, only a few are sufficiently central to warrant translation into diagnostic or therapeutic tools. Three recent developments have the potential to alter radically the discovery of molecular markers: the compendium of human genes; the advent of technologies that provide the means to identify simultaneously several known and unknown genes and proteins; and an appreciation of the critical processes involved in tumor initiation and progression.     

Proteomics technologies in endometriosis

Endometriosis is a common disorder that is associated with infertility and pelvic pain. Diagnosis is based on the visualization of endometriotic lesions during surgery as no reliable serum marker is currently available. The etiology of endometriosis is largely unknown. Over the last 20 years, several proteomics technologies have been used to research novel proteins with a potential etiological role in endometriosis, and to identify candidate serum markers for this condition. While some molecules identified by proteomics technologies may have a relevant role in the pathogenesis of endometriosis, the research of potential serum markers for this condition is still far from any clinical application. This review summarizes the state of the art and potential applications of proteomics in endometriosis research.     

Genome-Scale Discovery of DNA-Methylation Biomarkers for Blood-Based Detection of Colorectal Cancer

Background

There is an increasing demand for accurate biomarkers for early non-invasive colorectal cancer detection. We employed a genome-scale marker discovery method to identify and verify candidate DNA methylation biomarkers for blood-based detection of colorectal cancer.

Methodology/Principal Findings

We used DNA methylation data from 711 colorectal tumors, 53 matched adjacent-normal colonic tissue samples, 286 healthy blood samples and 4,201 tumor samples of 15 different cancer types. DNA methylation data were generated by the Illumina Infinium HumanMethylation27 and the HumanMethylation450 platforms, which determine the methylation status of 27,578 and 482,421 CpG sites respectively. We first performed a multistep marker selection to identify candidate markers with high methylation across all colorectal tumors while harboring low methylation in healthy samples and other cancer types. We then used pre-therapeutic plasma and serum samples from 107 colorectal cancer patients and 98 controls without colorectal cancer, confirmed by colonoscopy, to verify candidate markers. We selected two markers for further evaluation: methylated THBD (THBD-M) and methylated C9orf50 (C9orf50-M). When tested on clinical plasma and serum samples these markers outperformed carcinoembryonic antigen (CEA) serum measurement and resulted in a high sensitive and specific test performance for early colorectal cancer detection.

Conclusions/Significance

Our systematic marker discovery and verification study for blood-based DNA methylation markers resulted in two novel colorectal cancer biomarkers, THBD-M and C9orf50-M. THBD-M in particular showed promising performance in clinical samples, justifying its further optimization and clinical testing.     

Biomarker discovery in biological fluids

Discovery of novel protein biomarkers is essential for successful drug discovery and development. These novel protein biomarkers may aid accelerated drug efficacy, response, or toxicity decision making based on their enhanced sensitivity and/or specificity. These biomarkers, if necessary, could eventually be converted into novel diagnostic marker assays. Proteomic platforms developed over the past few years have given us the ability to rapidly identify novel protein biomarkers in various biological matrices from cell cultures (lysates, supernatants) to human clinical samples (serum, plasma, and urine). In this article, we delineate an approach to biomarker discovery. This approach is divided into three steps, (i) identification of markers, (ii) prioritization of identified markers, and (iii) preliminary validation (qualification) of prioritized markers. Using drug-induced idiosyncratic hepatotoxicity as a case study, the article elaborates methods and techniques utilized during the three steps of biomarker discovery process. The first step involves identification of markers using multi-dimensional protein identification technology. The second step involves prioritization of a subset of marker candidates based on several criteria such as availability of reagent set for assay development and literature association to disease biology. The last step of biomarker discovery involves development of preliminary assays to confirm the bio-analytical measurements from the first step, as well as qualify the marker(s) in pre-clinical models, to initiate future marker validation and development.     

A two-phase Bayesian methodology for the analysis of binary phenotypes in genome-wide association studies

Recent advances in sequencing and genotyping technologies are contributing to a data revolution in genome-wide association studies that is characterized by the challenging large p small n problem in statistics. That is, given these advances, many such studies now consider evaluating an extremely large number of genetic markers (p) genotyped on a small number of subjects (n). Given the dimension of the data, a joint analysis of the markers is often fraught with many challenges, while a marginal analysis is not sufficient. To overcome these obstacles, herein, we propose a Bayesian two-phase methodology that can be used to jointly relate genetic markers to binary traits while controlling for confounding. The first phase of our approach makes use of a marginal scan to identify a reduced set of candidate markers that are then evaluated jointly via a hierarchical model in the second phase. Final marker selection is accomplished through identifying a sparse estimator via a novel and computationally efficient maximum a posteriori estimation technique. We evaluate the performance of the proposed approach through extensive numerical studies, and consider a genome-wide application involving colorectal cancer.     

Identification of biomarkers for colorectal cancer through proteomics-based approaches

The early detection of colorectal cancer is one of the great challenges in the battle against this disease. However, owing to its heterogeneous character, single markers are not likely to provide sufficient diagnostic power to be used in colorectal cancer population screens. This review provides an overview of recent studies aimed at the discovery of new diagnostic protein markers through proteomics-based approaches. It indicates that studies that start with the proteomic analysis of tumor tissue or tumor cell lines (near the source) have a high potential to yield novel and colorectal cancer-specific biomarkers. In the next step, the diagnostic accuracy of these candidate markers can be assessed by a targeted ELISA assay using serum from colorectal cancer patients and healthy controls. Instead, direct proteomic analysis of serum yields predominantly secondary markers composed of fragments of abundant serum proteins that may be associated with tumor-associated protease activity, and alternatively, immunoproteomic analysis of the serum antibody repertoire provides a valuable tool to identify the molecular imprint of colorectal cancer-associated antigens directly from patient serum samples. The latter approach also allows a relatively easy translation into targeted assays. Eventually, multimarker assays should be developed to reach a diagnostic accuracy that meets the stringent criteria for colorectal cancer screening at the population level.     

Global and targeted quantitative proteomics for biomarker discovery

The extraordinary developments made in proteomic technologies in the past decade have enabled investigators to consider designing studies to search for diagnostic and therapeutic biomarkers by scanning complex proteome samples using unbiased methods. The major technology driving these studies is mass spectrometry (MS). The basic premises of most biomarker discovery studies is to use the high data-gathering capabilities of MS to compare biological samples obtained from healthy and disease-afflicted patients and identify proteins that are differentially abundant between the two specimen. To meet the need to compare the abundance of proteins in different samples, a number of quantitative approaches have been developed. In this article, many of these will be described with an emphasis on their advantageous and disadvantageous for the discovery of clinically useful biomarkers.     

Future tools for association mapping in crop plants

Association mapping currently relies on the identification of genetic markers. Several technologies have been adopted for genetic marker analysis, with single nucleotide polymorphisms (SNPs) being the most popular where a reasonable quantity of genome sequence data are available. We describe several tools we have developed for the discovery, annotation, and visualization of molecular markers for association mapping. These include autoSNPdb for SNP discovery from assembled sequence data; TAGdb for the identification of gene specific paired read Illumina GAII data; CMap3D for the comparison of mapped genetic and physical markers; and BAC and Gene Annotator for the online annotation of genes and genomic sequences.     

High-throughput human metabolism and toxicity analysis

Poor drug candidate safety profiles are often identified late in the drug development process, manifesting themselves in the preclinical and clinical phases and significantly contributing to the high cost and low yield of drug discovery. As a result, new tools are needed to accelerate the assessment of drug candidate toxicity and human metabolism earlier in the drug development process, from primary drug candidate screening to lead optimization. Although high-throughput screens exist for much of the discovery phase of drug development, translating such screening techniques into platforms that can accurately mimic the human in vivo response and predict the impact of drug candidates on human toxicology has proven difficult. Nevertheless, some success has been achieved in recent years, which may ultimately yield widespread acceptance in the pharmaceutical industry.     

Industrializing AI/ML during the end-to-end drug discovery process

Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several artificial intelligence and machine learning (AI/ML)-based drug discovery companies have attempted to build data-driven platforms spanning the end-to-end drug discovery process. The ability to identify elusive targets essentially leads to the diversification of discovery pipelines, thereby increasing the ability to address unmet needs. Modern ML technologies are complementing traditional computer-aided drug discovery by accelerating candidate optimization in innovative ways. This review summarizes recent developments in AI/ML methods from target identification to molecule optimization, and concludes with an overview of current industrial trends in end-to-end AI/ML platforms.     

Biomarker research with prospective study designs for the early detection of cancer

This article describes the principles of marker research with prospective studies along with examples for diagnostic tumor markers. A plethora of biomarkers have been claimed as useful for the early detection of cancer. However, disappointingly few biomarkers were approved for the detection of unrecognized disease, and even approved markers may lack a sound validation phase. Prospective studies aimed at the early detection of cancer are costly and long-lasting and therefore the bottleneck in marker research. They enroll a large number of clinically asymptomatic subjects and follow-up on incident cases. As invasive procedures cannot be applied to collect tissue samples from the target organ, biomarkers can only be determined in easily accessible body fluids. Marker levels increase during cancer development, with samples collected closer to the occurrence of symptoms or a clinical diagnosis being more informative than earlier samples. Only prospective designs allow the serial collection of pre-diagnostic samples. Their storage in a biobank upgrades cohort studies to serve for both, marker discovery and validation. Population-based cohort studies, which may collect a wealth of data, are commonly conducted with just one baseline investigation lacking serial samples. However, they can provide valuable information about factors that influence the marker level. Screening programs can be employed to archive serial samples but require significant efforts to collect samples and auxiliary data for marker research. Randomized controlled trials have the highest level of evidence in assessing a biomarker's benefit against usual care and present the most stringent design for the validation of promising markers as well as for the discovery of new markers. In summary, all kinds of prospective studies can benefit from a biobank as they can serve as a platform for biomarker research. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.     

Protein biomarker discovery and validation: the long and uncertain path to clinical utility

Better biomarkers are urgently needed to improve diagnosis, guide molecularly targeted therapy and monitor activity and therapeutic response across a wide spectrum of disease. Proteomics methods based on mass spectrometry hold special promise for the discovery of novel biomarkers that might form the foundation for new clinical blood tests, but to date their contribution to the diagnostic armamentarium has been disappointing. This is due in part to the lack of a coherent pipeline connecting marker discovery with well-established methods for validation. Advances in methods and technology now enable construction of a comprehensive biomarker pipeline from six essential process components: candidate discovery, qualification, verification, research assay optimization, biomarker validation and commercialization. Better understanding of the overall process of biomarker discovery and validation and of the challenges and strategies inherent in each phase should improve experimental study design, in turn increasing the efficiency of biomarker development and facilitating the delivery and deployment of novel clinical tests.     

Application of single nucleotide polymorphisms to non-model species: a technical review

Single nucleotide polymorphisms (SNPs) have gained wide use in humans and model species and are becoming the marker of choice for applications in other species. Technology that was developed for work in model species may provide useful tools for SNP discovery and genotyping in non-model organisms. However, SNP discovery can be expensive, labour intensive, and introduce ascertainment bias. In addition, the most efficient approaches to SNP discovery will depend on the research questions that the markers are to resolve as well as the focal species. We discuss advantages and disadvantages of several past and recent technologies for SNP discovery and genotyping and summarize a variety of SNP discovery and genotyping studies in ecology and evolution.