scholarly journals Predicting human pharmacokinetics from preclinical data: clearance

2021 ◽  
Vol 29 (2) ◽  
pp. 78
Author(s):  
Dong-Seok Yim ◽  
Soo Hyeon Bae ◽  
Suein Choi
Keyword(s):  
Human Pharmacokinetics ◽  
Preclinical Data

scholarly journals Predicting human pharmacokinetics from preclinical data: absorption

2020 ◽  
Vol 28 (3) ◽  
pp. 126
Author(s):  
Dong-Seok Yim ◽  
Suein Choi ◽  
Soo Hyeon Bae
Keyword(s):  
Human Pharmacokinetics ◽  
Preclinical Data

Pharmacokinetic Drug-drug Interaction of Antibiotics Used in Sepsis Care in China

2020 ◽  
Vol 21 ◽  
Author(s):  
Xuan Yu ◽  
Zixuan Chu ◽  
Jian Li ◽  
Rongrong He ◽  
Yaya Wang ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Penicillin G ◽  
Literature Mining ◽  
Human Pharmacokinetics ◽  
P450 Enzyme ◽  
Drug Drug Interaction ◽  
Pharmacokinetic Drug ◽  
Sepsis Care

Background: Many antibiotics have a high potential for having an interaction with drugs, as perpetrator and/or victim, in critically ill patients, and particularly in sepsis patients. Methods: The aim of this review is to summarize the pharmacokinetic drug-drug interaction (DDI) of 45 antibiotics commonly used in sepsis care in China. Literature mining was conducted to obtain human pharmacokinetics/dispositions of the antibiotics, their interactions with drug metabolizing enzymes or transporters, and their associated clinical drug interactions. Potential DDI is indicated by a DDI index > 0.1 for inhibition or a treated-cell/untreated-cell ratio of enzyme activity being > 2 for induction. Results: The literature-mined information on human pharmacokinetics of the identified antibiotics and their potential drug interactions is summarized. Conclusion: Antibiotic-perpetrated drug interactions, involving P450 enzyme inhibition, have been reported for four lipophilic antibacterials (ciprofloxacin, erythromycin, trimethoprim, and trimethoprim-sulfamethoxazole) and three lipophilic antifungals (fluconazole, itraconazole, and voriconazole). In addition, seven hydrophilic antibacterials (ceftriaxone, cefamandole, piperacillin, penicillin G, amikacin, metronidazole, and linezolid) inhibit drug transporters in vitro. Despite no reported clinical PK drug interactions with the transporters, caution is advised in the use of these antibacterials. Eight hydrophilic antibacterials (all β-lactams; meropenem, cefotaxime, cefazolin, piperacillin, ticarcillin, penicillin G, ampicillin, and flucloxacillin), are potential victims of drug interactions due to transporter inhibition. Rifampin is reported to perpetrate drug interactions by inducing CYP3A or inhibiting OATP1B; it is also reported to be a victim of drug interactions, due to the dual inhibition of CYP3A4 and OATP1B by indinavir. In addition, three antifungals (caspofungin, itraconazole, and voriconazole) are reported to be victims of drug interactions because of P450 enzyme induction. Reports for other antibiotics acting as victims in drug interactions are scarce.

Preclinical Data Supporting/Refuting the Use of Hypericum perforatum in the Treatment of Depression

2013 ◽  
Vol 12 (4) ◽  
pp. 474-486 ◽  
Author(s):  
Rosalia Crupi ◽  
Yousef Abusamra ◽  
Edoardo Spina ◽  
Gioacchino Calapai
Keyword(s):  
Hypericum Perforatum ◽  
Preclinical Data ◽  
Treatment Of Depression

scholarly journals Cell Therapy for Stroke: A Mechanistic Analysis

Neurosurgery ◽  
2020 ◽  
Author(s):  
Ben Jiahe Gu ◽  
David K Kung ◽  
Han-Chiao Isaac Chen
Keyword(s):  
Stem Cells ◽  
Clinical Trials ◽  
Cell Therapy ◽  
Clinical Translation ◽  
Stroke Survivors ◽  
Cell Replacement ◽  
Preclinical Data ◽  
Promising Strategy ◽  
Mechanistic Analysis

Abstract Cell therapy has been widely recognized as a promising strategy to enhance recovery in stroke survivors. However, despite an abundance of encouraging preclinical data, successful clinical translation remains elusive. As the field continues to advance, it is important to reexamine prior clinical trials in the context of their intended mechanisms, as this can inform future preclinical and translational efforts. In the present work, we review the major clinical trials of cell therapy for stroke and highlight a mechanistic shift between the earliest studies, which aimed to replace dead and damaged neurons, and later ones that focused on exploiting the various neuromodulatory effects afforded by stem cells. We discuss why both mechanisms are worth pursuing and emphasize the means through which cell replacement can still be achieved.

scholarly journals Improved cyclic urea inhibitors of the HIV-1 protease: synthesis, potency, resistance profile, human pharmacokinetics and X-ray crystal structure of DMP 450

1996 ◽  
Vol 3 (4) ◽  
pp. 301-314 ◽  
Author(s):  
C. Nicholas Hodge ◽  
Paul E. Aldrich ◽  
Lee T. Bacheler ◽  
Chong-Hwan Chang ◽  
Charles J. Eyermann ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Human Pharmacokinetics ◽  
Resistance Profile ◽  
X Ray ◽  
Hiv 1

Cardiac contractility assessment: A case study with clear impact and relevance of preclinical data

2012 ◽  
Vol 66 (2) ◽  
pp. 174 ◽  
Author(s):  
Pierre Lainée ◽  
Karen Philp ◽  
Alex Harmer ◽  
Matthew Bridgland-Taylor ◽  
Jackie Moors ◽  
...  
Keyword(s):  
Cardiac Contractility ◽  
Preclinical Data

scholarly journals Improving drug safety predictions by reducing poor analytical practices

2020 ◽  
Vol 4 ◽  
pp. 239784732097863
Author(s):  
Stanley E Lazic ◽  
Dominic P Williams
Keyword(s):  
At Risk ◽  
Drug Discovery ◽  
Drug Safety ◽  
Prediction Models ◽  
Predictive Modelling ◽  
Equal Weight ◽  
Negative Consequences ◽  
Preclinical Data ◽  
Safety Pharmacology ◽  
Patients At Risk

Predicting the safety of a drug from preclinical data is a major challenge in drug discovery, and progressing an unsafe compound into the clinic puts patients at risk and wastes resources. In drug safety pharmacology and related fields, methods and analytical decisions known to provide poor predictions are common and include creating arbitrary thresholds, binning continuous values, giving all assays equal weight, and multiple reuse of information. In addition, the metrics used to evaluate models often omit important criteria and models’ performance on new data are often not assessed rigorously. Prediction models with these problems are unlikely to perform well, and published models suffer from many of these issues. We describe these problems in detail, demonstrate their negative consequences, and propose simple solutions that are standard in other disciplines where predictive modelling is used.

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