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Related Concept Videos

Clearance Models: Compartment Models01:25

Clearance Models: Compartment Models

1.5K
Clearance measures drug elimination from the central compartment, including plasma and highly perfused organs like kidneys and liver. Its calculation varies depending on pharmacokinetic models and administration routes. The one-compartment model, for instance, portrays the pharmacokinetics of polar drugs such as aminoglycoside antibiotics administered intravenously and readily excreted in urine. In this case, clearance is influenced by the terminal rate constant (λz) and the total volume...
1.5K
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

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Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
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Clearance Models: Physiological Models01:09

Clearance Models: Physiological Models

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Drug clearance is a critical pharmacokinetic process involving the irreversible removal of drugs from the body through various organs over a specified time period. Physiological models are indispensable in determining organ-specific clearance, defined by the proportion of the drug eliminated per unit of time from the organ's blood volume.
The organ's clearance rate depends on the blood flow to the organ and the extraction ratio (E). The extraction ratio describes the organ's...
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Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

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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...
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Related Experiment Video

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Optimized PCR-based Detection of Mycoplasma
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Mycoplasma Clearance and Risk Analysis in a Model Bioprocess.

Julie Wang1, Sarah Johnson1, Matthew Brown1

  • 1Division II/Office of Biotechnology Products/Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Ave., Silver Spring, MD 20903. Views expressed in this article represent those of the authors and not necessarily policy or guidance from the U.S. Food and Drug Administration; and.

PDA Journal of Pharmaceutical Science and Technology
|January 17, 2017
PubMed
Summary

Mycoplasmas, cell wall-deficient bacteria, can contaminate biotechnology products. This study demonstrates that monoclonal antibody purification steps effectively remove and inactivate these contaminants, ensuring patient safety.

Keywords:
Acholeplasma laidlawiiBioprocessingCell cultureLow-pH holdMycoplasmaProtein A chromatographySolvent/detergentSpike/clearance

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Area of Science:

  • Biotechnology
  • Microbiology
  • Pharmaceutical Manufacturing

Background:

  • Mycoplasmas are cell wall-deficient bacteria that can contaminate cell cultures.
  • Their small size allows passage through 0.2 μm filters, posing a patient safety risk in biotechnology manufacturing.
  • Undetected mycoplasma contamination can compromise the safety and efficacy of biopharmaceuticals.

Purpose of the Study:

  • To evaluate the efficacy of standard monoclonal antibody (mAb) purification processes in clearing and inactivating mycoplasmas.
  • To assess the removal and killing capabilities of protein A chromatography, low-pH holds, and solvent/detergent treatments.
  • To ensure the safety of biotechnology-derived medicines by addressing potential mycoplasma contamination.

Main Methods:

  • Spike/removal studies using *Acholeplasma laidlawii* as a model mycoplasma.
  • Evaluation of protein A column chromatography for mycoplasma clearance.
  • Assessment of low-pH hold steps (glycine and acetate buffers) for mycoplasma inactivation.
  • Analysis of solvent/detergent treatment for mycoplasma killing efficacy.

Main Results:

  • Protein A chromatography achieved significant mycoplasma clearance (4-5 log10 reduction).
  • Column regeneration effectively prevented mycoplasma carryover between runs.
  • Low-pH hold steps inactivated *A. laidlawii* (LRV ≥4.57), and solvent/detergent treatment achieved complete inactivation (LRV ≥5.95).

Conclusions:

  • Typical monoclonal antibody purification operations effectively clear and inactivate mycoplasmas.
  • These manufacturing steps provide critical control points for ensuring the microbiological safety of biopharmaceuticals.
  • The findings enhance confidence in the safety assurance of biotechnology medicines for patients.