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Library: Topics in Applied Clinical Pharmacology

The topics are grouped in main sections, within each section there is a series of contributions set in ‚accordion‘ (expanding when you click on the item’s headline). Within a contribution you will find links either to other URL or to pdf’ed texts for download. For larger writings (e.g. ‚manuals‘) you may be asked to contact us to obtain access. This is a continuous project that will be updated regularly, also based on your feedback, comments and suggestions.

Calculators

General statistical resources

Samples size calculators

Statistical fragility Index
The fragility index is a measure of the robustness (or fragility) of the results of a clinical trial. The fragility index is a number indicating how many patients would be required to convert a trial from being statistically significant to not significant (p ≥ 0.05). The larger the fragility index the better (more robust) a trial’s data are. The intent of the fragility index is to be used in conjunction with the P value, 95% confidence interval, and various measures describing benefit or risk (relative risk reduction, absolute risk reduction, etc)

 

{27.Mar.2018 | ACPS-CdM}

Applied Clinical Pharmacokinetics - Guidelines

{27.Mar.2018 | ACPS-CdM}

BABE-reporting templates including BA-specification:

EEA | EMA

US | FDA

WHO

ASEAN

AUSTRALIA

CANADA

EGYPT

INDIA

JAPAN

RUSSIA

THAILAND: see ASEAN

{27.Mar.2018 | Update: 02.May.2018 | ACPS-CdM}

Applied Clinical Pharmacokinetics - Semantics

Establishing and maintaining blinding throughout the complex process of capturing and analysing data may be challenging, but avoids compromising objectivity. Bioanalytical (BA) determinations and pharmacokinetic (PK) analyses are at risk of being result-driven unless blinded. Nevertheless, blinding of such steps is uncommon and most often analysts are aware of the subject and treatment to whom the samples and PK-profiles belong.

Nevertheless, blinding could be established by:

  • Not disclosing the actual sample ID (subject, treatment, time after dosing) of blood/plasma samples by using coded sample IDs
  • To set strictly restrictive rulings on bioanalytical re-analysis
  • To disclose sample IDs only after completing all BA-determinations
  • Not disclosing the actual PK-profile ID (subject, treatment) of the time courses of the blood/plasma concentrations by using coded profile IDs
  • To disclose profile IDs only after completing all PK-analyses

We consider these steps to be highly recommendable; however, we are not aware of any guideline that encourages/enforces such blinding. We sometimes wonder why.

{27.Mar.2018 | ACPS-CdM}

Most regulatory pharmacokinetic (PK) arguments rely on summary values to describe and compare the time courses of the blood/plasma concentrations (the „PK-profile“) across treatments (e.g. formulations, doses, co-medications, co-morbidities, etc.).

To this purpose, the descriptors of the PK-profile should be chosen such that they are sufficient to identify meaningful differences in systemic exposure that might be relevant for the treatment outcome in terms of efficacy and safety.

This is generally accepted to be well covered by non-compartmental analysis (NCA). NCA relies on very basic non-pretentious mathematics and it does not impose model-assumptions; therefore NCA is unlikely to be confounded by whether the chosen mechanistic interpretation of the PK-profile is ‚correct‘ or not.

While relying on very basic mathematics it can be easily processed with unsophisticated software. There is a large range of PK software tools and packages, most of which also containing NCA: see for instance PharmPK-List of PK-Software.

Among these, we have excellent experience with PCModfit, which operates with all analytical steps well documented within a single platform; this is important for documenting the selection of the data points used to derive the apparent terminal log-linear disposition rate constant and half-life. This tool operates reliably and we have documented excellent agreement of its results with other software tools (incl. WinNonlin).

There is no regulatory ruling on using specific PK-software; however, it is a well-established urban myth that regulators would only accept analyses processed with the NCA-subroutines of expensive pharmacometric software platform packages such as Phoenix WinNonlin. Surely this package is reasonably claimed to be „The Industry Standard for PK/PD Modeling and Simulation“; however, the cost is high and out of proportion for just doing NCA.

{27.Mar.2018 | ACPS-CdM}

Most regulatory pharmacokinetic (PK) arguments rely on summary values to describe and compare the time courses of the blood/plasma concentrations (the „PK-profile“) across treatments (e.g. formulations, doses, co-medications, co-morbidities, etc.). This is generally accepted to be well covered by non-compartmental analysis (NCA). NCA relies on very basic non-pretentious mathematics and it does not impose model-assumptions; therefore is unlikely to be confounded by whether the chosen mechanistic interpretation of the PK-profile is ‚correct‘ or not.

NCA derives simple descriptors of the PK-profile: maximum observed concentrations (Cmax), time of occurrence of Cmax after dosing (tmax), quantifiable i.e. truncated area under the time course of the concentrations (AUC_tz), total i.e. extrapolated AUC (AUC_∞), apparent terminal disposition rate constant (λ), apparent terminal disposition half-life (t½), quantifiable and total area under the statistical first moment curve (AUMC), and mean residence time (MRT).

Among the NCA-criteria, Cmax and AUC_∞ are of prime relevance while representing the peak and total (area) systemic exposure; the MRT is a highly robust expression of the timely distribution of the AUC (since it relies on all profiling points).

Also, the AUC_∞ (=F.Dose/CL) is particularly useful since it allows quantifying differences in the fractional amount of systemic bioavailability (F) across treatments if the clearance (CL) can be accepted to be treatment-independent.
In this context, Cmax/AUC_∞ is useful since it allows qualifying differences in the rate of systemic bioavailability while reflecting changes in Cmax disproportionate to changes in the amount of bioavailability.

The AUC_∞ expresses the balance between the amount of systemically bioavailable drug (F.Dose) and the disposition thereof (CL). The latter can be calculated from the AUC if F is known: CL = F.Dose/AUC_∞; this is useful only for intravascular dosing (iv-bolus or iv-infusion); in contrast, for extravascular dosing F is generally incomplete to an unknown extent.
Nevertheless, NCA-programs often calculate Dose/AUC_∞ and report it as CL/F. This so-called ‚apparent oral clearance‘ is not meaningful since it results from two ‚unknowns‘ (F and CL) and most certainly does not reflect actual CL or changes thereof in any meaningful fashion. If anything, CL/F is nothing but the reciprocal of the dose-normalised AUC_∞.

Another such oddity is that NCA-programs often report Vd/F (‚apparent oral distribution volume‘), which can only be used as an estimate of the distribution volume (Vd) if F were known (Vd/F = CL/λ or Vd/F = CL.MRT).

We advocate not using CL/F or Vd/F unless F is reliably known. This is particularly important in SmPC-descriptions of a drug’s PK-behaviour since the terms are often used without clearly specifying them as ‚apparent‘ in this context.

{09.May.2018 | ACPS-CdM }