Comparing a Statistical Model and Bayesian Approach to Establish the Design Space for the Coating of Ciprofloxacin HCl Beads at Different Scales of Production.

The primary objective of this study was to compare two methods for establishing a design space for critical process parameters that affect ethylcellulose film coating of multiparticulate beads and assess this design space validity across manufacturing scales. While there are many factors that can affect film coating, this study will focus on the effects processing conditions have on the quality and extent of film formation, as evaluated by their impact coating yield and drug release. Ciprofloxacin HCl layered beads were utilized as an active substrate core, ethylcellulose aqueous dispersion as a controlled release polymer, and triethyl citrate as a plasticizer. Thirty experiments were conducted using a central composite design to optimize the coating process and map the response surface to build a design space using either statistical least squares or a Bayesian approach. The response surface was fitted using a linear two-factor interaction model with spraying temperature, curing temperature, and curing time as significant model terms. The design spaces established by the two approaches were in close agreement with the statistical least squares approach being more conservative than the Bayesian approach. The design space established for the critical process parameters using small-scale batches was tested using scale-up batches and found to be scale-independent. The robustness of the design space was confirmed across scales and was successfully utilized to establish process signature for the coating process.

A Systematic Approach of Employing Quality by Design Principles: Risk Assessment and Design of Experiments to Demonstrate Process Understanding and Identify the Critical Process Parameters for Coating of the Ethylcellulose Pseudolatex Dispersion Using Non-Conventional Fluid Bed Process.

The goal of this study was to utilize risk assessment techniques and statistical design of experiments (DoE) to gain process understanding and to identify critical process parameters for the manufacture of controlled release multiparticulate beads using a novel disk-jet fluid bed technology. The material attributes and process parameters were systematically assessed using the Ishikawa fish bone diagram and failure mode and effect analysis (FMEA) risk assessment methods. The high risk attributes identified by the FMEA analysis were further explored using resolution V fractional factorial design. To gain an understanding of the processing parameters, a resolution V fractional factorial study was conducted. Using knowledge gained from the resolution V study, a resolution IV fractional factorial study was conducted; the purpose of this IV study was to identify the critical process parameters (CPP) that impact the critical quality attributes and understand the influence of these parameters on film formation. For both studies, the microclimate, atomization pressure, inlet air volume, product temperature (during spraying and curing), curing time, and percent solids in the coating solutions were studied. The responses evaluated were percent agglomeration, percent fines, percent yield, bead aspect ratio, median particle size diameter (d50), assay, and drug release rate. Pyrobuttons® were used to record real-time temperature and humidity changes in the fluid bed. The risk assessment methods and process analytical tools helped to understand the novel disk-jet technology and to systematically develop models of the coating process parameters like process efficiency and the extent of curing during the coating process.

Patient-Centric Quality Standards.

To ensure the quality, safety and efficacy of medicinal products, it is necessary to develop and execute appropriate manufacturing process and product control strategies. Traditionally, product control strategies have focused on testing known quality attributes with limits derived from levels administered in preclinical and clinical studies with an associated statistical analysis to account for variability. However, not all quality attributes have impact to the patient and those with the potential to impact safety and efficacy may not be significant when dosed at patient-centric levels. Therefore, achieving patient-centricity is understanding patient relevance, which is defined as the level of impact that a quality attribute could have on safety and efficacy within the potential exposure range. A patient-centric quality standard (PCQS) is therefore a set of patient relevant attributes and their associated acceptance ranges to which a drug product should conform within the expected patient exposure range. This manuscript describes historical perspectives details the way to create and leverage a PCQS in a variety of pharmaceutical product modalities.

Re-Envisioning Pharmaceutical Manufacturing: Increasing Agility for Global Patient Access.

The traditional paradigm for pharmaceutical manufacturing is focused primarily upon centralized facilities that enable mass production and distribution. While this system reliably maintains high product quality and reproducibility, its rigidity imposes limitations upon new manufacturing innovations that could improve efficiency and support supply chain resiliency. Agile manufacturing methodologies, which leverage flexibility through portability and decentralization, allow manufacturers to respond to patient needs on demand and present a potential solution to enable timely access to critical medicines. Agile approaches are particularly applicable to the production of small-batch, personalized therapies, which must be customized for each individual patient close to the point-of-care. However, despite significant progress in the advancement of agile-enabling technologies across several different industries, there are substantial global regulatory challenges that encumber the adoption of agile manufacturing techniques in the pharmaceutical industry. This review provides an overview of regulatory barriers as well as emerging opportunities to facilitate the use of agile manufacturing for the production of pharmaceutical products. Future-oriented approaches for incorporating agile methodologies within the global regulatory framework are also proposed. Collaboration between regulators and manufacturers to cohesively navigate the regulatory waters is ultimately needed to best serve patients in the rapidly-changing healthcare environment.

Class modeling analysis of heparin 1H NMR spectral data using the soft independent modeling of class analogy and unequal class modeling techniques.

To differentiate heparin samples with varying amounts of dermatan sulfate (DS) impurities and oversulfated chondroitin sulfate (OSCS) contaminants, proton NMR spectral data for heparin sodium active pharmaceutical ingredient samples from different manufacturers were analyzed using multivariate chemometric techniques. A total of 168 samples were divided into three groups: (a) Heparin, [DS] ≤ 1.0% and [OSCS] = 0%; (b) DS, [DS] > 1.0% and [OSCS] = 0%; (c) OSCS, [OSCS] > 0% with any content of DS. The chemometric models were constructed and validated using two well-established methods: soft independent modeling of class analogy (SIMCA) and unequal class modeling (UNEQ). While SIMCA modeling was conducted using the entire set of variables extracted from the NMR spectral data, UNEQ modeling was combined with variable reduction using stepwise linear discriminant analysis to comply with the requirement that the number of samples per class exceed the number of variables in the model by at least 3-fold. Comparison of the results from these two modeling approaches revealed that UNEQ had greater sensitivity (fewer false positives) while SIMCA had greater specificity (fewer false negatives). For Heparin, DS, and OSCS, respectively, the sensitivity was 78% (56/72), 74% (37/50), and 85% (39/46) from SIMCA modeling and 88% (63/72), 90% (45/50), and 91% (42/46) from UNEQ modeling. Importantly, the specificity of both the SIMCA and UNEQ models was 100% (46/46) for Heparin with respect to OSCS; no OSCS-containing sample was misclassified as Heparin. The specificity of the SIMCA model (45/50, or 90%) was superior to that of the UNEQ model (27/50, or 54%) for Heparin with respect to DS samples. However, the overall prediction ability of the UNEQ model (85%) was notably better than that of the SIMCA model (76%) for the Heparin vs DS vs OSCS classes. The models were challenged with blends of heparin spiked with nonsulfated, partially sulfated, or fully oversulfated chondroitin sulfate A, dermatan sulfate, or heparan sulfate at the 1.0, 5.0, and 10.0 wt % levels. The results from the present study indicate that the combination of (1)H NMR spectral data and class modeling techniques (viz., SIMCA and UNEQ) represents a promising strategy for assessing the quality of commercial heparin samples with respect to impurities and contaminants. The methodologies show utility for applications beyond heparin to other complex products.

Determination of galactosamine impurities in heparin samples by multivariate regression analysis of their (1)H NMR spectra.

Heparin, a widely used anticoagulant primarily extracted from animal sources, contains varying amounts of galactosamine impurities. Currently, the United States Pharmacopeia (USP) monograph for heparin purity specifies that the weight percent of galactosamine (%Gal) may not exceed 1%. In the present study, multivariate regression (MVR) analysis of (1)H NMR spectral data obtained from heparin samples was employed to build quantitative models for the prediction of %Gal. MVR analysis was conducted using four separate methods: multiple linear regression, ridge regression, partial least squares regression, and support vector regression (SVR). Genetic algorithms and stepwise selection methods were applied for variable selection. In each case, two separate prediction models were constructed: a global model based on dataset A which contained the full range (0-10%) of galactosamine in the samples and a local model based on the subset dataset B for which the galactosamine level (0-2%) spanned the 1% USP limit. All four regression methods performed equally well for dataset A with low prediction errors under optimal conditions, whereas SVR was clearly superior among the four methods for dataset B. The results from this study show that (1)H NMR spectroscopy, already a USP requirement for the screening of contaminants in heparin, may offer utility as a rapid method for quantitative determination of %Gal in heparin samples when used in conjunction with MVR approaches.

Identification of heparin samples that contain impurities or contaminants by chemometric pattern recognition analysis of proton NMR spectral data.

Chemometric analysis of a set of one-dimensional (1D) (1)H nuclear magnetic resonance (NMR) spectral data for heparin sodium active pharmaceutical ingredient (API) samples was employed to distinguish USP-grade heparin samples from those containing oversulfated chondroitin sulfate (OSCS) contaminant and/or unacceptable levels of dermatan sulfate (DS) impurity. Three chemometric pattern recognition approaches were implemented: classification and regression tree (CART), artificial neural network (ANN), and support vector machine (SVM). Heparin sodium samples from various manufacturers were analyzed in 2008 and 2009 by 1D (1)H NMR, strong anion-exchange high-performance liquid chromatography, and percent galactosamine in total hexosamine tests. Based on these data, the samples were divided into three groups: Heparin, DS ≤ 1.0% and OSCS = 0%; DS, DS > 1.0% and OSCS = 0%; and OSCS, OSCS > 0% with any content of DS. Three data sets corresponding to different chemical shift regions (1.95-2.20, 3.10-5.70, and 1.95-5.70 ppm) were evaluated. While all three chemometric approaches were able to effectively model the data in the 1.95-2.20 ppm region, SVM was found to substantially outperform CART and ANN for data in the 3.10-5.70 ppm region in terms of classification success rate. A 100% prediction rate was frequently achieved for discrimination between heparin and OSCS samples. The majority of classification errors between heparin and DS involved cases where the DS content was close to the 1.0% DS borderline between the two classes. When these borderline samples were removed, nearly perfect classification results were attained. Satisfactory results were achieved when the resulting models were challenged by test samples containing blends of heparin APIs spiked with non-, partially, or fully oversulfated chondroitin sulfate A, heparan sulfate, or DS at the 1.0%, 5.0%, and 10.0% (w/w) levels. This study demonstrated that the combination of 1D (1)H NMR spectroscopy with multivariate chemometric methods is a nonsubjective, statistics-based approach for heparin quality control and purity assessment that, once standardized, minimizes the need for expert analysts.

Analysis of Drugs in Oral Fluid Using LC-MS/MS.

Oral fluid analysis for drugs is increasingly used in a variety of testing areas: pain management and medication monitoring, parole and probation situations, driving under the influence of drugs (DUID), therapeutic drug monitoring, and testing for drugs in the workplace. The sample collection itself is straightforward, rapid, observable, and noninvasive, requiring no special facilities (compared to urine) or medical personnel (compared to blood). The pH of saliva is slightly acidic relative to blood; therefore, drugs which are more basic tend to be present in higher concentration in oral fluid than in blood: cocaine, amphetamines, oxycodone, tramadol, buprenorphine, methadone, and fentanyl. Conversely, acidic drugs and drugs which are strongly protein bound have lower concentrations in oral fluid than in blood: examples include benzodiazepines, barbiturates, and carisoprodol. Because of the low volume of specimen available for analysis and the drug concentrations present (generally much lower than those in urine), efficient extraction methods and sensitive confirmation procedures are necessary for routine analysis of drugs in oral fluid. In this chapter, solid-phase extraction methods are described for a variety of drugs with liquid chromatography-tandem mass spectrometry detection.

Roadside drug testing: An evaluation of the Alere DDS® 2 mobile test system.

The number of drivers using drugs has increased over the last few years, and is likely to continue its upward trend. Testing drivers for alcohol use is routine and standardized, but the same is not true for the identification of driving under the influence of drugs (DUID). The Drug Evaluation and Classification Program (DECP) was developed to train police officers to recognize the signs and symptoms of recent drug use and remains an invaluable program; however, there are insufficient numbers of these highly trained drug recognition experts (DREs) available to attend every potential drug involved traffic incident. While blood and urine samples are used to test for drugs in a driver, both have disadvantages, particularly as they pertain to the length of time required after a traffic stop to sample collection. Therefore, the development of oral fluid testing devices which can be operated at the roadside and have the potential to assist officers in the identification of drug use is a major advancement in DUID cases. This project evaluated the performance of one instrumental oral fluid roadside testing device (Alere DDS®2) compared to DRE opinion, oral fluid laboratory-based analysis, and routine blood testing. The results showed that there was a good correlation with DRE observations and the device performance was >80% in all drug categories compared to laboratory-based analytical testing, both in oral fluid and blood, with few exceptions. The instrument can be considered a useful tool to assist law enforcement in identifying a drugged driver. Because the device does not test for all potentially impairing drugs, the opinion of the police officer regarding the condition of the driver should still be considered the most important aspect for arrest and further action.

A new chapter in pharmaceutical manufacturing: 3D-printed drug products.

FDA recently approved a 3D-printed drug product in August 2015, which is indicative of a new chapter for pharmaceutical manufacturing. This review article summarizes progress with 3D printed drug products and discusses process development for solid oral dosage forms. 3D printing is a layer-by-layer process capable of producing 3D drug products from digital designs. Traditional pharmaceutical processes, such as tablet compression, have been used for decades with established regulatory pathways. These processes are well understood, but antiquated in terms of process capability and manufacturing flexibility. 3D printing, as a platform technology, has competitive advantages for complex products, personalized products, and products made on-demand. These advantages create opportunities for improving the safety, efficacy, and accessibility of medicines. Although 3D printing differs from traditional manufacturing processes for solid oral dosage forms, risk-based process development is feasible. This review highlights how product and process understanding can facilitate the development of a control strategy for different 3D printing methods. Overall, the authors believe that the recent approval of a 3D printed drug product will stimulate continual innovation in pharmaceutical manufacturing technology. FDA encourages the development of advanced manufacturing technologies, including 3D-printing, using science- and risk-based approaches.

Microfluidic ethanol biobatteries on a microchip.

This chapter outlines the methods and procedures for making a microfluidic and microfabricated biofuel cell. Commercially available screen-printing carbon inks are employed as electrodes by micromolding them onto glass microchips. The carbon ink electrodes are modified with methylene green and alcohol dehydrogenase immobilized within a modified Nafion membrane to act as bioanodes in the microfluidic system. The complete biofuel cell produces power using an external platinum cathode and an integrated microfluidic bioanode. Miniaturization of power sources, such as biofuel cells, is important in applications for implementation in small technologies (i.e., sensors, bioreactors, and lab-on-a-chip technology).

Microchip-based ethanol/oxygen biofuel cell.

One of the limitations of lab-on-a-chip technology has been the lack of integrated power supplies for powering various devices on the chip. This research focused on design of a stackable, microchip-based biofuel cell. The biofuel cell is powered by the addition of ethanol through a flow channel to a bioanode. The bioanode contains a micromolded carbon ink anode that has been modified with two layers. The first layer is poly(methylene green), which is an electrocatalyst for NADH oxidation; the second layer is a membrane that contains an immobilized enzyme, alcohol dehydrogenase. Each layer was characterized electrochemically. It was found that the poly(methylene green) layer is kinetically-limited, but when the complete bioanode is formed, the bioanode is diffusion-limited due to slow mass transport of NADH within the modified Nafion membrane. When used relative to an external platinum cathode, the biofuel cell showed maximum open circuit potentials of 0.34 V and maximum current densities of 53.0 +/- 9.1 microA cm(-2). This research demonstrates the feasibility of a microfabricated biofuel cell device.

Detection of ketamine and norketamine in urine of nonhuman primates after a single dose of ketamine using microplate enzyme-linked immunosorbent assay (ELISA) and NCI-GC-MS.

The general anesthetic ketamine (Ketalar, Ketaject, Vetalar) (KET) is used in human and veterinary medicine for induction of anesthesia for short surgical procedures and routine veterinary examination. Its illicit use by teenagers in rave parties has been reported, and it has recently been identified as a substance associated with sexual assault. One aim of this paper was to study the elimination of KET and its major metabolite norketamine (NKET) in urine collected from five nonhuman primates that received a single dose (5 mg/kg, I.M.) of KET and to study elimination patterns to determine how long after drug administration KET and NKET can be detected. Another aim of this study was to develop and validate a highly sensitive negative ion chemical ionization-gas chromatography-mass spectrometry (NCI-GC-MS) method for the simultaneous quantitation of KET and its major metabolite NKET in urine and to analyze urine samples collected from the animals. The last aim of this study was to apply and evaluate a newly developed ELISA screening methodology for detection of KET and its metabolites in the same urine samples collected from primates which received a single dose of KET. In two monkeys, KET was detected in urine up to 3 days after drug administration (32-7070 ng/mL); in one monkey, it was detected up to 4 days (65-13,500 ng/mL); in one monkey, it was detected only on days 1 and 2 (4000 and 70 ng/mL, respectively); and in one monkey, it was detected 10 days after KET injection (22-35,000 ng/mL). NKET concentrations ranged from 63 pg/mL to 1.75 microg/mL, and it remained in the urine throughout the entire 35-day study period in 4 out of 5 animals. In one monkey, NKET was detected up to 31 days after KET administration. Urine analysis using ELISA revealed that KET and NKET can be easily detectable at 25 ng/mL. In one monkey, KET and its metabolites were detected in urine up to 4 days after drug administration, up to 7 days in two monkeys, up to 11 days in one monkey, and 16 days after KET injection in one monkey. Urine extraction followed by screening using ELISA methodology allowed for significant extension of the detection period in all animals from the study. It is believed that the KET elimination in urine of nonhuman primates is slightly faster than in humans. We propose that NCI-GC-MS be employed to detect NKET as a target compound in urine in toxicological investigations of drug-facilitated sexual assault when KET use by the perpetrator is suspected.

Advancing Product Quality: a Summary of the Inaugural FDA/PQRI Conference.

On September 16 and 17, 2014, the Food and Drug Administration (FDA) and Product Quality Research Institute (PQRI) inaugurated their Conference on Evolving Product Quality. The Conference is conceived as an annual forum in which scientists from regulatory agencies, industry, and academia may exchange viewpoints and work together to advance pharmaceutical quality. This Conference Summary Report highlights key topics of this conference, including (1) risk-based approaches to pharmaceutical development, manufacturing, regulatory assessment, and post-approval changes; (2) FDA-proposed quality metrics for products, facilities, and quality management systems; (3) performance-based quality assessment and clinically relevant specifications; (4) recent developments and implementation of continuous manufacturing processes, question-based review, and European Medicines Agency (EMA)-FDA pilot for Quality-by-Design (QbD) applications; and (5) breakthrough therapies, biosimilars, and international harmonization, focusing on ICH M7 and Q3D guidelines. The second FDA/PQRI conference on advancing product quality is planned for October 5-7, 2015.

Deposition of 7-aminoclonazepam and clonazepam in hair following a single dose of Klonopin.

The objective of this paper was to determine whether benzodiazepine clonazepam (CLO) and its major metabolite 7-aminoclonazepam (7-ACLO) could be detected in hair collected from healthy volunteers after receiving a single 3-mg dose of Klonopin (clonazepam). Such data would be of great importance to law enforcement agencies trying to determine the best time interval for hair collection from a victim of drug-facilitated sexual assault (DFSA) in order to reveal drug use. Ten healthy volunteers (6 women and 4 men, 23-49 years old) participated in the study. The following hair samples were collected from each volunteer: one before CLO administration, and 1, 3, 5, 14, 21, and 28 days after. All hair samples were pulverized and 50-mg aliquots were sonicated in methanol and digested with 0.1 N HCl at 55 degrees C for 18-24 h. Internal standard, diazepam-d5 (DIAZ-d5) was used. Both extracts were combined and extracted using HCX solid-phase extraction columns. After derivatization with HFBA all extracts were analyzed using highly sensitive negative chemical ionization gas chrometography-mass spectrometry. Standard curves for CLO (20-100 pg/mg) and 7-ACLO (1-20 pg/mg) were prepared by spiking aliquots (50 mg) of negative hair and had correlation coefficients of 0.985 and 0.989, respectively. In addition, two levels of control hair were prepared for CLO and 7-ACLO. All method validation parameters were within acceptable limits. 7-ACLO was detected in hair of 6 out of 10 volunteers. In two cases 7-ACLO appeared in hair three days after CLO intake and remained detectable for the entire 28-day study period (3.6-8.4 pg/mg and 2.7-3.0 pg/mg), and in two subjects it was detectable 21 days later (4.9 and 2.7 pg/mg and 1.2 and 23 pg/mg). In two volunteers 7-ACLO was detected only on day 28 (1.8 and 3.3 pg/mg). CLO was not detected in any of the samples.

Chemometric analysis for identification of botanical raw materials for pharmaceutical use: a case study using Panax notoginseng.

The overall control of the quality of botanical drugs starts from the botanical raw material, continues through preparation of the botanical drug substance and culminates with the botanical drug product. Chromatographic and spectroscopic fingerprinting has been widely used as a tool for the quality control of herbal/botanical medicines. However, discussions are still on-going on whether a single technique provides adequate information to control the quality of botanical drugs. In this study, high performance liquid chromatography (HPLC), ultra performance liquid chromatography (UPLC), capillary electrophoresis (CE) and near infrared spectroscopy (NIR) were used to generate fingerprints of different plant parts of Panax notoginseng. The power of these chromatographic and spectroscopic techniques to evaluate the identity of botanical raw materials were further compared and investigated in light of the capability to distinguishing different parts of Panax notoginseng. Principal component analysis (PCA) and clustering results showed that samples were classified better when UPLC- and HPLC-based fingerprints were employed, which suggested that UPLC- and HPLC-based fingerprinting are superior to CE- and NIR-based fingerprinting. The UPLC- and HPLC- based fingerprinting with PCA were able to correctly distinguish between samples sourced from rhizomes and main root. Using chemometrics and its ability to distinguish between different plant parts could be a powerful tool to help assure the identity and quality of the botanical raw materials and to support the safety and efficacy of the botanical drug products.

Combining (1)H NMR spectroscopy and chemometrics to identify heparin samples that may possess dermatan sulfate (DS) impurities or oversulfated chondroitin sulfate (OSCS) contaminants.

Heparin is a naturally produced, heterogeneous compound consisting of variably sulfated and acetylated repeating disaccharide units. The structural complexity of heparin complicates efforts to assess the purity of the compound, especially when differentiating between similar glycosaminoglycans. Recently, heparin sodium contaminated with oversulfated chondroitin sulfate A (OSCS) has been associated with a rapid and acute onset of an anaphylactic reaction. In addition, naturally occurring dermatan sulfate (DS) was found to be present in these and other heparin samples as an impurity due to incomplete purification. The present study was undertaken to determine whether chemometric analysis of these NMR spectral data would be useful for discrimination between USP-grade samples of heparin sodium API and those deemed unacceptable based on their levels of DS, OSCS, or both. Several multivariate chemometric methods for clustering and classification were evaluated; specifically, principal components analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and the k-nearest-neighbor (kNN) method. Data dimension reduction and variable selection techniques, implemented to avoid over-fitting the training set data, markedly improved the performance of the classification models. Under optimal conditions, a perfect classification (100% success rate) was attained on external test sets for the Heparin vs OSCS model. The predictive rates for the Heparin vs DS, Heparin vs [DS+OSCS], and Heparin vs DS vs OSCS models were 89%, 93%, and 90%, respectively. In most cases, misclassifications can be ascribed to the similarity in NMR chemical shifts of heparin and DS. Among the chemometric methods evaluated in this study, we found that the LDA models were superior to the PLS-DA and kNN models for classification. Taken together, the present results demonstrate the utility of chemometric methods when applied in combination with (1)H NMR spectral analysis for evaluating the quality of heparin APIs.

Identification and quantification of nicotine biomarkers in human oral fluid from individuals receiving low-dose transdermal nicotine: a preliminary study.

The objective of this preliminary study was to identify and quantify potential nicotine (NIC) biomarkers in post-exposure oral fluid samples collected from 10 NIC-abstinent human participants administered 7 mg transdermal NIC using liquid chromatography-tandem mass spectrometry (LC-MS-MS). Oral fluid samples were collected prior to NIC patch application and at 0.5 and 0.75 h after patch removal using the Quantisal() oral fluid collection device. The validated LC-MS-MS analyte panel included nicotine-Nbeta-D-glucuronide, cotinine-N-oxide, trans-3-hydroxycotinine, norcotinine, trans-nicotine-1'-N-oxide, cotinine (COT), nornicotine, NIC, anatabine, anabasine, and cotinine-N-beta-D-glucuronide. Analytes and corresponding deuterated internal standards were extracted by solid-phase extraction. NIC and COT concentrations were quantifiable in oral fluid samples collected from 6 of the 10 participants 0.5 h after patch removal and in oral fluid samples collected from 7 of the 10 participants 0.75 h after patch removal. Based on the mean NIC and COT concentrations in oral fluid and plasma for the participants with both quantifiable NIC and COT at the 0.5 and 0.75 h collection times, the oral fluid-plasma ratio was 6.4 for NIC and 3.3 for COT. An ELISA procedure was also validated and successfully applied as a screening tool for these oral fluid samples in conjunction with LC-MS-MS confirmation. An ELISA cut-off concentration of 5.0 ng/mL provided excellent sensitivity for discrimination of COT-positive post-exposure oral fluid samples collected after low-level transdermal NIC exposure and oral fluid samples collected prior to patch application.

Zolpidem : Forensic aspects for the toxicologist and pathologist.

Zolpidem (Ambien), a nonbenzodiazepine hypnotic, is indicated for the short-term treatment of insomnia. Because it has become the most widely prescribed sleep aid in the United States, it is being increasingly detected in a variety of forensic specimens, and its interaction with other medications is being questioned. We present a comprehensive overview of the basic chemistry, pharmacokinetics, and pharmacodynamics of zolpidem; its interaction with other prescription drugs; its effects on driving and motor skills; and its analysis and interpretation in forensic casework.