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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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The Michaelis–Menten equation is a fundamental model for describing capacity-limited kinetics in drug metabolism. It offers insights into the rate of decline of plasma drug concentration Cp over time, with Vmax and KM as pivotal parameters.
Vmax represents the maximum achievable process rate, while KM, known as the Michaelis constant, signifies the drug concentration at which the process rate reaches half its maximum. This relationship between Vmax, KM, and Cp gives rise to three distinct...

Pharmacokinetic–Pharmacodynamic Relationship: Model Components

Pharmacokinetic–Pharmacodynamic Relationship: Model Components

Pharmacokinetic-pharmacodynamic (PK–PD) modeling is essential in drug development and clinical pharmacology. It provides a quantitative framework to predict drug behavior and response over time. This approach integrates pharmacokinetics (PK), which describes the drug's absorption, distribution, metabolism, and excretion, with pharmacodynamics (PD), which characterizes the drug’s biological effects and mechanisms of action.The disposition kinetics of a drug determine its plasma...

Pharmacodynamic Models: Linear Concentration–Effect Model

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The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing...

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PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure...

Microbial Bioremediation of Pesticides

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Pesticides often feature structurally complex chemical architectures, incorporating halogen groups and multiple aromatic rings. These characteristics confer high chemical stability, rendering many pesticides resistant to natural degradation processes. This resistance poses significant environmental concerns, as persistent pesticide residues can accumulate in ecosystems and affect non-target organisms.Despite the inherent stability of many pesticides, certain microorganisms possess the metabolic...

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Related Experiment Video

Updated: May 5, 2026

Identification of Pharmaceuticals in The Aquatic Environment Using HPLC-ESI-Q-TOF-MS and Elimination of Erythromycin Through Photo-Induced Degradation

Identification of Pharmaceuticals in The Aquatic Environment Using HPLC-ESI-Q-TOF-MS and Elimination of Erythromycin Through Photo-Induced Degradation

Published on: August 1, 2018

Biodegradation of ciprofloxacin using machine learning tools: Kinetics and modelling.

Neha Kamal¹, Amal Krishna Saha², Ekta Singh¹

¹Aquatic Toxicology Laboratory, Environmental Toxicology Group, Food, Drug & Chemical, Environment and Systems, Toxicology (FEST) Division, Council of Scientific and Industrial Research-Indian Institute of Toxicology Research (CSIR-IITR), Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India.

Journal of Hazardous Materials

|April 2, 2024

Summary

This study explores microbial biodegradation of ciprofloxacin, a common antibiotic pollutant. Optimized bacterial consortia achieved 95.5% degradation, offering a sustainable solution for antibiotic wastewater remediation.

Keywords:

Biodegradation Emerging organic contaminants Machine learning tools Sustainable management Wastewater treatment

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Last Updated: May 5, 2026

Identification of Pharmaceuticals in The Aquatic Environment Using HPLC-ESI-Q-TOF-MS and Elimination of Erythromycin Through Photo-Induced Degradation

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Published on: August 1, 2018

Visualization of Bacterial Resistance using Fluorescent Antibiotic Probes

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Metabolic Profiling to Determine Bactericidal or Bacteriostatic Effects of New Natural Products using Isothermal Microcalorimetry

Metabolic Profiling to Determine Bactericidal or Bacteriostatic Effects of New Natural Products using Isothermal Microcalorimetry

Published on: October 29, 2020

Area of Science:

Environmental microbiology
Biotechnology
Wastewater treatment

Background:

Antibiotic pollution poses significant ecological and health risks, driving the emergence of multidrug-resistant bacteria.
Conventional wastewater treatment methods face limitations in cost and efficacy for removing emerging organic pollutants.
Biodegradation presents a sustainable, cost-effective alternative for environmental remediation.

Purpose of the Study:

To investigate the biodegradation of ciprofloxacin using microbial consortia.
To identify optimal conditions for ciprofloxacin biodegradation via metabolic pathways.
To evaluate the efficiency of machine learning tools in optimizing biodegradation processes.

Main Methods:

Utilized microbial consortia for ciprofloxacin biodegradation.
Employed Artificial Neural Network (ANN) and Response Surface Methodology (RSM) with Box-Behnken design (BBD) for parameter optimization.
Assessed biodegradation efficiency under optimized culture conditions.

Main Results:

Designed bacterial consortia achieved 95.5% ciprofloxacin degradation under optimal conditions.
Model predictions closely matched experimental results: 95.20% (RSM) and 94.53% (ANN).
Optimized biodegradation process demonstrated high efficiency in removing ciprofloxacin.

Conclusions:

Microbial biodegradation is a highly effective method for removing ciprofloxacin from aqueous media.
Machine learning tools successfully optimized biodegradation parameters, enhancing efficiency.
This approach offers a greener, sustainable solution for antibiotic-contaminated wastewater treatment.