Spurious, falsely-labeled, falsified or counterfeit (SFFC) pharmaceuticals are a health concern that claims hundreds of thousands of lives annually1, a violation of intellectual property rights which cost legitimate companies billions2, and a low-risk high yield revenue stream for organized crime2. While ports of entry and border control points are the primary access control points for SFFC3,4, advances in field portable detection and equipment offers an increasingly effective method for the assessment of pharmaceuticals at regional centers and points of distribution. This is particularly important for less developed countries (LDC) who do not maintain satellite or regional testing facilities. As part of a proposed protocol to assess field portable detection equipment, an ultrafast liquid chromatography, tandem mass spectrometry (UFLC-MS/MS) method for the quantification of liquid formulation Oxytocin was developed. The six minute method was found to have a within run %bias of +/-16%, a linear dynamic range of 150-1000 nanograms/milliliter (ng/ml), and an accuracy within acceptability criteria for all tested concentrations. The effectiveness of three identified transition ions, 723.1, 86.2 and 70.1 Daltons, for the analysis of oxytocin by mass spectrometry was assessed across several figures of merit to include signal to noise ratio, %CV, calibration sensitivity, and analytical sensitivity. The 723.1 ion fragment was recommended for quantification, while the 70.1 dalton ion was recommended as a qualifier ion, although 86.2 also performed within acceptability criteria. A method for the UFLC-MS/MS assessment of degradation products for oxytocin was proposed for specificity testing. Degradation of oxytocin by exposure to highly acidic, basic, and thermal conditions for one hour was attempted. Formation of degraded products was not observed. Additionally, existing High Performance Liquid Chromatography (HPLC) methods for the simultaneous assessment of Artesunate and Amodiaquine HCl were modified to assess compatibility with UFLC. No method assessed produced sufficient quality signal to continue with method development.

No description available.

Nonribosomal peptides (NRP) are highlighted in pharmacological studies as novel NRPs are often promising substances for new drug development. To effectively discover novel NRPs from microbial fermentations, a crucial step is to identify known NRPs in an early stage and exclude them from further investigation. This so-called dereplication step ensures the scarce resource is only spent on the novel NRPs in the following up experiments. Tandem mass spectrometry has been routinely used for NRP dereplication. However, few bioinformatics tools have been developed to computationally identify NRP compounds from mass spectra, while manual identification is currently the roadblock hindering the throughput of novel NRP discovery. In this thesis, we review the nature of nonribosomal peptides and investigate the challenges in computationally solving the identification problem. After that, iSNAP software is proposed as an automated and high throughput solution for tandem mass spectrometry based NRP identification. The algorithm has been evolved from the traditional database search approach for identifying sequential peptides, to one that is competent at handling complicated NRP structures. It is designed to be capable of identifying mixtures of NRP compounds from LC-MS/MS of complex extract, and also finding structural analogs which differ from an identified known NRP compound with one monomer. Combined with an in-house NRP structural database of 1107 compounds, iSNAP is tested to be an effective tool for mass spectrometry based NRP identification. The software is available as a web service at http://monod.uwaterloo.ca/isnap for the research community.

High-throughput techniques are currently some of the most promising methods to study molecular biology, with the potential to improve medicine and enable new biological applications. In proteomics, the large scale study of proteins, the leading method is mass spectrometry. At present researchers can routinely identify and quantify thousands of proteins in a single experiment with the technique called shotgun proteomics. A challenge of these experiments is the computational analysis and the interpretation of the mass spectra. A shotgun proteomics experiment easily generates tens of thousands of spectra, each thought to represent a peptide from a protein. Due to the immense biological and technical complexity, however, our computational tools often misinterpret these spectra and derive incorrect peptides. As a consequence, the biological interpretation of the experiment relies heavily on the statistical confidence that we estimate for the identifications. In this thesis, I have included four articles from my research on the accuracy of the statistical confidence estimates in shotgun proteomics, how to accomplish and evaluate it. In the first two papers a new method to use pre-characterized protein samples to evaluate this accuracy is presented. The third paper deals with how to avoid statistical inaccuracies when using machine learning techniques to analyze the data. In the fourth paper, we present a new tool for analyzing shotgun proteomics results, and evaluate the accuracy of  its statistical estimates using the method from the first papers. The work I have included here can facilitate the development of new and accurate computational tools in mass spectrometry-based proteomics. Such tools will help making the interpretation of the spectra and the downstream biological conclusions more reliable.

Tandem mass spectrometry (MS/MS) is a powerful tool for identifying peptide sequences. In a typical experiment, incorrect peptide identifications may result due to noise contained in the MS/MS spectra and to the low quality of the spectra. Filtering methods are widely used to remove the noise and improve the quality of the spectra before the subsequent spectra identification process. However, existing filtering methods often use features and empirically assigned weights. These weights may not reflect the reality that the contribution (reflected by weight) of each feature may vary from dataset to dataset. Therefore, filtering methods that can adapt to different datasets have the potential to improve peptide identification results. This thesis proposes two adaptive filtering methods; denoising and quality assessment, both of which improve efficiency and effectiveness of peptide identification. First, the denoising approach employs an adaptive method for picking signal peaks that is more suitable for the datasets of interest. By applying the approach to two tandem mass spectra datasets, about 66% of peaks (likely noise peaks) can be removed. The number of peptides identified later by peptide identification on those datasets increased by 14% and 23%, respectively, compared to previous work (Ding et al., 2009a). Second, the quality assessment method estimates the probabilities of spectra being high quality based on quality assessments of the individual features. The probabilities are estimated by solving a constraint optimization problem. Experimental results on two datasets illustrate that searching only the high-quality tandem spectra determined using this method saves about 56% and 62% of database searching time and loses 9% of high-quality spectra. Finally, the thesis suggests future research directions including feature selection and clustering of peptides.

Nonribosomal peptides (NRP) are highlighted in pharmacological studies as novel NRPs are often promising substances for new drug development. To effectively discover novel NRPs from microbial fermentations, a crucial step is to identify known NRPs in an early stage and exclude them from further investigation. This so-called dereplication step ensures the scarce resource is only spent on the novel NRPs in the following up experiments. Tandem mass spectrometry has been routinely used for NRP dereplication. However, few bioinformatics tools have been developed to computationally identify NRP compounds from mass spectra, while manual identification is currently the roadblock hindering the throughput of novel NRP discovery. In this thesis, we review the nature of nonribosomal peptides and investigate the challenges in computationally solving the identification problem. After that, iSNAP software is proposed as an automated and high throughput solution for tandem mass spectrometry based NRP identification. The algorithm has been evolved from the traditional database search approach for identifying sequential peptides, to one that is competent at handling complicated NRP structures. It is designed to be capable of identifying mixtures of NRP compounds from LC-MS/MS of complex extract, and also finding structural analogs which differ from an identified known NRP compound with one monomer. Combined with an in-house NRP structural database of 1107 compounds, iSNAP is tested to be an effective tool for mass spectrometry based NRP identification. The software is available as a web service at http://monod.uwaterloo.ca/isnap for the research community.

Thesis (Ph. D.)--Ohio State University, 2007. / Full text release at OhioLINK's ETD Center delayed at author's request

Thesis (M.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 47-51). Also available on the World Wide Web.

Drugs of Abuse (DOAs) are among the single largest contributor to crime in the United States and present a high cost to society in terms of financial costs and physical/mental well-being of individuals. The forensic community requires a variety of validated methods to detect and analyze DOAs in a variety of different sample types, and most developed methods utilize a liquid or gas chromatography (GC or LC) separation system paired to a mass spectrometer (MS) detection detector. Capillary Electrophoresis (CE) based separation techniques have also been experimented with due to this technique’s high efficiency and speed, high resolving power, low sample consumption, and potentially lower cost when compared to GC or LC based techniques, even though the sensitivity of these systems is perceived to be weaker. The goal of this research to develop a CE-MS/MS method utilizing the ZipChipTM to demonstrate it can accurately and reliably detect and quantify DOAs. The DOAs analyzed for this method were opioids and benzodiazepines, and these were 6-monacetylmorphine, 7-aminoclonazepam, codeine, diazepam, dihydrocodeine, 2-Ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine fentanyl, heroin, hydrocodone, hydromorphone, meperidine, methadone, morphine, norfentanyl, oxycodone, and oxymorphone. Standard Practices for Method Validation in Forensic Toxicology guidelines from the Academy Standards Board (ASB) of Toxicology were used as the template for this validation; samples were prepared and analyzed as neat standards in diluent, blood and urine were assessed for interferences, ionization suppression/enhancement, and extraction recovery. The total runtime for the method was 3.5 minutes, with the retention time range being 1.4 to 2.9 minutes. All samples were prepared using compound standards diluted in metabolite diluent, which consisted of methanol, ammonium acetate, and water prior to injection. The calibration curves consisted of eight calibrator samples that ranged from 0.5 ng/ml to 200 ng/ml for all analytes, and a linear model was used for each compound. The minimum acceptable 𝑅2 value was set to >0.98, and each curve had a weighing factor of 1𝑥2. Each curve for most of the compounds achieved the minimum requirement apart from two Codeine curves (0.9781 and 0.9785) and 7-aminoclonazepam (0.9791). Bias and precision were assessed at three concentrations- 5, 100, and 150 ng/ml. The minimum requirement for bias and precision for a compound was if the percent bias or coefficient of variation was within +/- 20%. Most compounds in this method exhibit acceptable levels of bias (except for Dihydrocodeine which had a bias of 24.58% at 100 ng/ml), and the only compounds to meet the minimum requirement for precision were 6-MAM, 7-aminoclonazepam, diazepam, fentanyl, methadone, and morphine. The limit of detection and limit of quantitation were both set at the lowest calibrator level of 0.5 ng/ml, and no carryover was observed in this method. No interferences occurred due to both deuterated internal standards and from common compounds such as benzylecogine, cocaine, and lidocaine, but blood cause signal interference with fentanyl and urine caused signal interference with methadone and norfentanyl. Ionization suppression and enhancement was observed for a majority of the compounds, and this observation will need to be assessed as to the effect it has on validation parameters in the future. The results collected suggest that accurate, reliable, and sensitive data may be collected if a compound has a specifically paired deuterated internal standard included in the sample. The speed of the suggested method and the minimal sample preparation could be desirable for forensic use. Further testing will need to be conducted to fully validate this method for blood and urine.

The opioid epidemic has become a serious public health problem in the United States. The increasing abuse of synthetic opioids has raised concerns in the society. Fentanyl is a synthetic opioid analgesic which has resulted in an increasing number of drug overdoses since 2013. In addition, fentanyl analogs, originally manufactured for use as analgesics or animal tranquilizers, have emerged in the United States drug market. Fentanyl and its analogs, similar to other opioids, work as full µ-agonists, binding with µ-receptors in the brain. Fentanyl and its analogs elicit more potent effects compared to the traditional opioids being abused such as morphine or heroin. With the emergence of fentanyl analogs in the drug market, identifying and differentiating those analogs becomes a challenge due to their structural similarities to fentanyl. The purpose of this research was to develop a method of identifying and quantifying nine fentanyl analogs in urine and oral fluid using the QSight® Triple Quad LC-MS/MS, coupled with a Halo® C18, 2.7µm column. The method was validated based on AAFS Standards Board (ASB) Standard 036, Standard Practices for Method Validation in Forensic Toxicology. The analytes in this research included fentanyl, norfentanyl, acetyl fentanyl, carfentanil, cyclopropyl fentanyl, methoxyacetyl fentanyl, valeryl fentanyl, furanyl fentanyl and 4-anilino-N-phenethylpiperdine (4ANPP). All samples, calibrators, and quality controls (QC) were prepared by spiking certified reference standards into donated human urine or human oral fluid. Supported liquid extraction (SLE) was performed as the sample preparation method using ISOLUTE® SLE+ 1mL columns followed by evaporation. All samples were reconstituted with 200 µL methanol. The mobile phases used in this method were 5mM ammonium formate in Millipore water with 0.1% formic acid and methanol with 0.1% formic acid. A 10-minute LC method achieved complete resolution of the analytes, with specific retention times ranging from 3.5 to 5.7 minutes. For urine and oral fluid analysis, the calibration range for all analytes was established from 1 to 70 ng/mL. The resulting r2 values were greater than 0.988 for all analytes. Bias and precision were evaluated at 3, 25 and 60 ng/mL, and bias and percent coefficient of variation (%CV) for within and between run precision had acceptable values within ±20%. The limit of detection (LOD) was 0.1 ng/mL for most fentanyl analogs, with a LOD of 0.01 ng/mL for valeryl fentanyl and furanyl fentanyl. Carryover was not detected for any analytes in either matrix. Recovery of all compounds following SLE for both urine and oral fluid was above 50%. For urine, the ion enhancement and suppression of all analytes was within 25%. For oral fluid, the ion enhancement and suppression of most analytes was within 25% except valeryl fentanyl, which experienced suppression of 35%. The matrices analyzed had no interference effect on the detection or quantitation of analytes in this method. The interference effects of different commonly encountered drugs were studied and showed minimal impacts on the results generated from this method. All analytes were stable for up to 72 hours at room temperature, except cyclopropyl fentanyl. In conclusion, using the QSight® Triple Quad LC-MS/MS following SLE effectively identified and quantified fentanyl analogs present in both urine and oral fluid. This method has shown its potential to be applied to casework samples for fentanyl analogs detection.