Modelagem farmacocinética e análise de sistemas lineares para a predição da concentração de medicamentos no corpo humano.
Bibliographic record
Abstract
The pharmacokinetic modeling can predict the concentration of drug in different tissues of the human body.The development of mathematical models is an important tool to verify the appropriateness of certain procedures performed in medication administration.The objective of this work is to develop a pharmacokinetic model able to predict the plasma concentration of drug in the body after various forms of infusion.Two approaches were used.Initially, in the one-compartment approach it was considered that the drug enters the body directly into the blood compartment, which represents the entire human body.In the two-compartment approach it was considered the following compartments: one representing the means by which the drug is infused into the body (either via the gastrointestinal tract, lung, or transdermal) and one representing the blood plasma.In both cases, it was considered homogeneous concentration of the drug in the compartments.The model was built by using block diagrams and the solution was obtained using the Laplace Transform.The model was validated by comparing its results to literature data, with very good agreement.The model allowed comparing the one-compartment constant infusion of drug in the body with the periodic infusion.The analysis of the results generated by the model showed that the concentrations achieved by these methods are not the same.The two-compartment model allowed simulating oral and transdermal administration, and inhalation.It was possible to predict blood concentration after interruption of therapy with anti-depressants and anti-conceptional drugs.The model was able to verify the time it takes to reach the former level.Methods have been proposed to achieve the same concentration in a shorter period of time.Another application was the comparison of the treatment with whole tablets and taken by half in a smaller interval of time.It was found that the concentration achieved is different even though the same mass is ingested in both cases.The model was also used to calculate the concentration of nicotine after cigarette smoking and it was found that the individual who smokes every three hours, nicotine is not entirely eliminated from body.Furthermore, it was possible to simulate overdose of an anti-inflammatory and the period of time when the concentration is above the therapeutic level.It has been proposed a method to obtain pharmacokinetic parameter related to absorption, which can be easily obtained based on data present in the drug bull.This method is much simpler and more accurate than the method proposed in the, which uses graphical analysis and clinical data that are not so easy to be obtained.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".