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Limbic Cortico-Striato-Pallido-Pontine Substrates of Sensorimotor Gating in Animal Models and Psychiatric Disorders
Published in Peter W. Kalivas, Charles D. Barnes, Limbic Motor Circuits and Neuropsychiatry, 2019
Neal R. Swerdlow, David L. Braff, S. Barak Caine, Mark A. Geyer
Studies using systemic drug administration have revealed that PPI is potently modulated by changes in brain dopamine (DA) and glutamate activity. In rats, PPI is reduced by drugs that stimulate brain DA activity, including the direct DA agonist apomorphine (APO)14–16 and the indirect DA agonist d-amphetamine (AMPH),16,17 and cocaine (R. Mansbach and M. Geyer, unpublished observation). These effects are reversed by DA receptor antagonists.15,16PPI is disrupted when AMPH is administered either acutely or chronically,16 or by the substituted congeners of methamphetamine, MDMA and MDEA.18 The effects of APO on PPI are evident at low doses (0.25–0.4 mg/kg sc) that do not produce behavioral activation,14–17 while high doses of APO and AMPH increase startle reflex amplitude. For example, low doses of APO that have no significant effect on startle amplitude virtually eliminate PPI,15 while drugs that significantly increase startle amplitude (e.g., haloperidol)16 do not decrease PPI. These observations suggest that separate neural substrates are responsible for DA-mediated effects on startle amplitude and PPI.
The Need for Clinical Guidelines on NPS
Published in Ornella Corazza, Andres Roman-Urrestarazu, Handbook of Novel Psychoactive Substances, 2018
Owen Bowden-Jones, Dima Abdulrahim
Cocaine**, cocaine derivatives and piperazine, methamphetamine, mephedrone, methylenedioxypyrovalerone (MDPV), butylone, ethcathinone, ethylone, 3- and 4-fluoromethcathinone, methedrone, methylone, pyrovalerone, 3-MeOMC, 3-MMC, 4-BMC, 4-MEC, 4-MeO-a-PVP, 4-MeO-PBP, 4-MeO-PV9, 4-MPD, 4F-PV8, 4FPV9, 4F-PVP, a-PBT, a-PHP, a-PVT, dibutylone, DL-4662, ethylone, MDPPP, MOPPP, NEB, pentedrone, MDMA, MDEA, methylone (bk-MDM), bk-MBDB (beta-ketone-MBDB), Butylone PMA, PMMA, 5-APB, 6-APB, 5-APDB, 6-APDB, 5-MAPB, 6-MAPB, 5-EAPB, 5-APD.
Ciprofloxacin
Published in M. Lindsay Grayson, Sara E. Cosgrove, Suzanne M. Crowe, M. Lindsay Grayson, William Hope, James S. McCarthy, John Mills, Johan W. Mouton, David L. Paterson, Kucers’ The Use of Antibiotics, 2017
Jason Kwong, M. Lindsay Grayson
Multiple resistance mechanisms account for ciprofloxacin resistance in both S. aureus and S. epidermidis. Altered GyrA and GrlA (known as ParC in E. coli) subunits are found in the majority of resistant isolates, with common alterations being Ser80Phe in GrlA and Ser84Leu in GyrA (Kwak et al., 2013; Linde et al., 2001; Munoz Bellido et al., 1999; Gordon et al., 2014); although genes encoding efflux pumps have also been frequently noted. NorA, NorB and NorC are efflux pumps belonging to the major facilitator superfamily (MFS) of transporters and overexpression of these pumps has been shown to produce 4- to 8-fold increases in ciprofloxacin MIC (Hooper and Jacoby, 2015). Hydrophilic quinolones, such as ciprofloxacin and norfloxacin, are substrates of NorA, NorB, and NorC, while hydrophobic quinolones such as moxifloxacin and sparfloxacin are substrates of only NorB and NorC (Truong-Bolduc et al., 2005; Truong-Bolduc et al., 2006; Yu et al., 2002). Regulation of these efflux pumps is complex, with MgrA and NorG being the best and most recently studied of the regulator proteins (Truong-Bolduc et al., 2011; Truong-Bolduc et al., 2005; Truong-Bolduc et al., 2006). Overexpression of other transporters has also been shown to reduce susceptibility to quinolones, including MdeA (norfloxacin, ciprofloxacin), SdrM (norfloxacin), QacB(III) (norfloxacin, ciprofloxacin), LmrS (gatifloxacin), and MepA (norfloxacin, ciprofloxacin, moxifloxacin, sparfloxacin) (Floyd et al., 2010; Huang et al., 2004; Kaatz et al., 2006; Nakaminami et al., 2010; Yamada et al., 2006), while plasmid-mediated multidrug efflux pumps such QacB have also demonstrated effects on fluoroquinolone susceptibility in S. aureus (Nakaminami et al., 2010).
Underreporting of drug use among electronic dance music party attendees
Published in Clinical Toxicology, 2021
Joseph J. Palamar, Alberto Salomone, Katherine M. Keyes
Participants were asked about demographic characteristics, frequency of past-year EDM party attendance, and past-year use of >90 drugs. Drugs queried included cannabis, cocaine, MDMA/ecstasy/Molly, LSD, methamphetamine, ketamine, DMT, 2 C series drugs, synthetic cathinones (“bath salts”, including ethylone), MDEA, heroin, and fentanyl. They were also asked about nonmedical use of amphetamine and prescription opioids. Nonmedical use was defined for participants as use without a prescription or in a manner in which it was not prescribed. Test-retest reliability of our drug use questions has been shown to be high (κ = 0.88-1.00) [39]. At the end of the survey, participants were asked how many of questions on the survey they answered honestly.
Entering the era of computationally driven drug development
Published in Drug Metabolism Reviews, 2020
Neha Maharao, Victor Antontsev, Matthew Wright, Jyotika Varshney
PBPK models can be utilized to test the mechanistic underpinnings of physiological processes that impact compound ADME, and to enable a more accurate prediction of drug disposition based on diverse population factors, such as ethnicity, age, and disease condition to identify ideal dosing regimens for first-in-human studies and toxicological assessments (Sager et al. 2015). SimCYP, GastroPlus, and PK-SIM are some of the widely used commercial products for building PBPK models. Indeed, there are several examples of the use of PBPK models in regulatory submissions and labeling recommendations for novel compounds as outlined in the review by Yoshida et al. (2017). PBPK models are also extensively used to predict putative drug–drug interactions (DDI) (Yoshida et al. 2017). An excellent study by Chen et al. elaborates on the use of PBPK model to evaluate a complex DDI involving a circulating inhibitory metabolite (mono-desethyl-amiodarone, MDEA), of the perpetrator drug amiodarone and their effect on the exposure of a victim drug simvastatin (Chen et al. 2015). The authors use a combination of bottom-up and top-down approach to simulate the plasma-time profiles of amiodarone and MDEA and the accumulation of these chemical entities following chronic administration. The model successfully predicted CYP450 inhibition of several CYP substrates (simvastatin, dextromethorphan, and warfarin) mediated by amiodarone and its metabolite. The authors further extend the application of the mixed bottom-up and top-down modeling approach to predict the interaction potential of an antibody-conjugated cytotoxic agent—monomethyl auristatin E (Chen et al. 2015). Thus, the utility of PBPK modeling is no longer limited to the conventional small molecules but can be successfully applied to large molecule PK predictions.
Tetracaine from urethral ointment causes false positive amphetamine results by immunoassay
Published in Clinical Toxicology, 2021
Robin Wijngaard, Marina Parra-Robert, Lourdes Marés, Anna Escalante, Emilio Salgado, Bernardino González-de-la-Presa, Jordi To-Figueras, Mercè Brunet
The amphetamine screening test consisted of one-point calibration with d-methamphetamine (1000 µg/L) (CEDIA Multi-Drug calibrator; Thermo Fisher Scientific, Waltham, MA, USA). The assay expresses a high cross-reactivity against dl-amphetamine (88%), dl-methamphetamine (116%), 3,4-methylendioxy-amphetamine (MDA) (116%), MDMA (196%), 3,4-methylenedioxy-ethylamphetamine (MDEA) (172%) and p-methoxymethamphetamine (PMMA) (100%). The amphetamine IA results were expressed as positive or negative using a cut-off point of 1000 µg/L.