Adaptive dosing methods and feedback dosing MCQs With Answer

Adaptive dosing and feedback dosing are central to precision pharmacotherapy, especially in therapeutic drug monitoring (TDM). This collection of MCQs is designed for M.Pharm students to deepen understanding of model-informed dose adjustment, Bayesian forecasting, population pharmacokinetics, and real-time feedback loops used to optimize drug exposure. Questions emphasize practical decision-making: interpreting concentration data, selecting sampling times, implementing Bayesian priors, handling assay and residual error, and applying AUC- or trough-guided strategies. The set blends conceptual and applied scenarios to prepare students for clinical TDM implementation, research in adaptive control, and regulatory considerations in individualized therapy.

Q1. Which principle best describes adaptive dosing in the context of therapeutic drug monitoring?

• Fixed-dose escalation at fixed intervals regardless of concentrations
• Adjusting doses based on measured drug concentrations, patient response, and pharmacokinetic models
• Using only population average doses without individual measurements
• Switching drugs if side effects occur without measuring levels

Correct Answer: Adjusting doses based on measured drug concentrations, patient response, and pharmacokinetic models

Q2. What is the primary advantage of Bayesian forecasting in feedback dosing?

• It requires no prior information about patients
• It provides individualized parameter estimates by combining prior population information with individual concentration data
• It always eliminates analytical assay error
• It guarantees immediate therapeutic success without monitoring

Correct Answer: It provides individualized parameter estimates by combining prior population information with individual concentration data

Q3. In model-informed precision dosing (MIPD), what role does the population pharmacokinetic (popPK) model play?

• It replaces therapeutic drug monitoring entirely
• It provides a prior distribution of PK parameters used to interpret individual concentration data
• It is only used for drug discovery, not dosing
• It standardizes assay calibration across laboratories

Correct Answer: It provides a prior distribution of PK parameters used to interpret individual concentration data

Q4. When designing a limited sampling strategy for AUC-guided dosing, which consideration is most important?

• Maximizing the number of samples regardless of timing
• Selecting timepoints that capture absorption and elimination phases to accurately estimate AUC
• Sampling only at trough because AUC can be approximated from trough alone for all drugs
• Choosing timepoints based solely on patient convenience

Correct Answer: Selecting timepoints that capture absorption and elimination phases to accurately estimate AUC

Q5. Which statement about feedback control dosing (closed-loop) is correct?

• It requires no pharmacokinetic model and relies only on clinician intuition
• It continuously adjusts dosing using measured concentrations and a control algorithm to reach target exposure
• It is identical to empiric titration based on adverse effects
• It ignores inter-occasion variability to simplify dosing

Correct Answer: It continuously adjusts dosing using measured concentrations and a control algorithm to reach target exposure

Q6. Which type of variability is most directly reduced by adaptive dosing informed by TDM?

• Inter-occasion variability in laboratory equipment
• Inter-individual pharmacokinetic variability by individualizing dose to measured exposure
• Analytical imprecision due to assay performance
• Population-level covariate effects

Correct Answer: Inter-individual pharmacokinetic variability by individualizing dose to measured exposure

Q7. In Bayesian dose adaptation, what is the effect of a highly informative prior compared with sparse individual data?

• The posterior estimate will reflect primarily the individual data and ignore the prior
• The posterior will be strongly influenced by the prior, potentially underweighting the new individual observations
• The posterior becomes non-informative and meaningless
• The prior and data are averaged equally regardless of information content

Correct Answer: The posterior will be strongly influenced by the prior, potentially underweighting the new individual observations

Q8. Which metric is most appropriate when target attainment is defined by AUC/MIC for an antibiotic?

• Single trough concentration only
• Area under the concentration–time curve over 24 hours (AUC24) divided by minimum inhibitory concentration (MIC)
• Peak-to-trough ratio exclusively
• Time above MIC only without measuring AUC

Correct Answer: Area under the concentration–time curve over 24 hours (AUC24) divided by minimum inhibitory concentration (MIC)

Q9. Which approach best handles assay measurement error when performing feedback dosing?

• Assume no measurement error and adjust dosing aggressively
• Incorporate assay variability into the likelihood function of the PK model used for Bayesian updating
• Discard any concentrations with suspected error and do not use them
• Double the measured concentrations to account for underestimation

Correct Answer: Incorporate assay variability into the likelihood function of the PK model used for Bayesian updating

Q10. Which sampling time is generally least informative for estimating both clearance and volume in a one-compartment IV bolus model?

• Very early timepoint during absorption/distribution phase
• Multiple evenly spaced samples spanning the dosing interval
• A single random mid-interval trough sample only
• A combined early peak and late trough sampling scheme

Correct Answer: A single random mid-interval trough sample only

Q11. What is the main goal of adaptive feedback dosing in drugs with narrow therapeutic indices?

• To maintain every patient at the population mean concentration
• To maximize pharmacodynamic variability between patients
• To achieve individual target exposure that balances efficacy and toxicity
• To avoid any therapeutic drug monitoring

Correct Answer: To achieve individual target exposure that balances efficacy and toxicity

Q12. Which statement about Empirical Bayes Estimates (EBE) used in TDM is true?

• EBEs are computed without regard to population priors
• EBEs shrink individual parameter estimates toward the population mean based on data information content
• EBEs eliminate residual unexplained variability
• EBEs are always identical to true individual parameters

Correct Answer: EBEs shrink individual parameter estimates toward the population mean based on data information content

Q13. When might trough concentration monitoring be inadequate for dosing decisions?

• When the pharmacodynamic index is AUC-based and absorption/elimination rates vary significantly
• When the drug has linear kinetics and steady state is achieved
• When the target is trough-dependent toxicity only
• When only adherence is being assessed

Correct Answer: When the pharmacodynamic index is AUC-based and absorption/elimination rates vary significantly

Q14. Which adaptive dosing strategy is most appropriate for drugs with important inter-occasion variability (e.g., fluctuating renal function)?

• One-time baseline dose based on body weight with no monitoring
• Frequent re-evaluation with updated Bayesian forecasting using the most recent concentrations
• Fixed-dose regimen without adjustments across occasions
• Switching to a different drug class without reassessing exposure

Correct Answer: Frequent re-evaluation with updated Bayesian forecasting using the most recent concentrations

Q15. In a feedback dosing algorithm, what is the purpose of defining a target concentration range or target AUC?

• To provide an arbitrary reference with no link to outcomes
• To serve as the control objective used by the algorithm to adjust doses toward optimal exposure
• To reduce workload by eliminating monitoring
• To ensure everyone receives the highest possible dose

Correct Answer: To serve as the control objective used by the algorithm to adjust doses toward optimal exposure

Q16. Which consideration is essential when implementing adaptive dosing in pediatrics?

• Pediatric patients always require the same adult priors without modification
• Accounting for maturation, weight-based scaling, and age-dependent clearance in the PK model
• Assuming renal function is constant across all ages
• Using the largest possible sampling volume for accuracy

Correct Answer: Accounting for maturation, weight-based scaling, and age-dependent clearance in the PK model

Q17. What is the likely consequence of ignoring time-varying covariates (e.g., changing creatinine) in adaptive dosing?

• No impact on dose optimization
• Potential systematic bias in predicted exposure and suboptimal dosing decisions
• Improved accuracy of Bayesian predictions
• Guaranteed safety because models assume stability

Correct Answer: Potential systematic bias in predicted exposure and suboptimal dosing decisions

Q18. Which algorithmic feature improves safety in automated feedback dosing systems?

• Unbounded dose changes based solely on a single outlying concentration
• Dose-change limits, decision rules for data plausibility, and clinician override capabilities
• Removal of any monitoring to rely on the algorithm alone
• Automatic switching to experimental drugs when targets are missed

Correct Answer: Dose-change limits, decision rules for data plausibility, and clinician override capabilities

Q19. How does prior predictive checks contribute to building a model used for adaptive dosing?

• They are unnecessary once a model is published
• They evaluate whether the model and priors generate plausible concentration-time profiles before using the model clinically
• They eliminate the need for external validation
• They replace the requirement for measuring concentrations in practice

Correct Answer: They evaluate whether the model and priors generate plausible concentration-time profiles before using the model clinically

Q20. Which performance metric is most appropriate to evaluate an adaptive dosing strategy in a clinical trial?

• Percentage of patients achieving predefined exposure targets (e.g., AUC within target window)
• Total number of samples collected irrespective of exposure
• The average assay runtime in the laboratory
• The number of different dosing algorithms published

Correct Answer: Percentage of patients achieving predefined exposure targets (e.g., AUC within target window)

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