About this Research Topic
The importance of these methods has increased in recent years; for example, saliva can be applied in therapeutic drug monitoring (TDM) and pharmacokinetic studies. Saliva samples are easier and safer to collect compared to blood samples. This is of particular value in pediatric or geriatric populations and for patients who need to collect samples at home.
Given the low levels of analytes that are usually present in samples obtained by non-invasive methods, it is necessary to develop and validate reliable, sensitive analytical methods for the determination of drugs and their metabolites in samples of this kind.
The application of analytical techniques such as liquid chromatography (LC), gas chromatography (GC), capillary electrophoresis (CE), and mass spectrometry (MS) has much to offer in this context.
In addition to non-invasive sampling methods, oral administration of drugs poses unique challenges and opportunities for improving our understanding of drug metabolism and transport following ingestion. Oral administration is preferred due to its convenience and patient compliance, but it requires comprehensive studies on bioavailability, absorption, and first-pass metabolism to ensure efficacy and safety.
Combining non-invasive methods with oral administration studies could provide a more holistic understanding of drug metabolism and transport within the body.
This Research Topic aims to assess the application of non-invasive methods and oral administration for the determination of drugs and their metabolites in biological samples. Potential topics include, but are not limited to:
- Development of modern analytical methods
- Pharmacokinetics
- Bioequivalence
- Therapeutic drug monitoring
- Drug metabolism studies
- Advances in non-invasive sampling techniques
- Impact of oral administration on drug metabolism and transport
Keywords: Therapeutic drug monitoring, pharmacokinetics, bioequivalence, drug determination, biological samples, saliva analysis, urinalysis, hair analysis, drug metabolism
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