PK/PD Data Analysis - Macrolide Antibiotic
Updated: May 25, 2021
The provided (not included) comes from a single dose oral study in man. It is for a new macrolide antibiotic that is designed for widespread use in general practice. Assume that the toxicology studies needed to support this study have shown nothing unusual for a macrolide.
Plot the plasma concentration profiles for each of the subjects and describe the data in terms of the PK parameters Tmax, Cmax, half-life, Kelim, apparent clearance, and AUC. Calculate the means and standard deviations for each, except Tmax for which the median and interquartile range should be tabulated. Comment on any outlying values.
An assignment by: Darren Wogman MSc. Completed as part of Pharmaceutical Medicine MSc at King's College London
Figure 1 shows the plasma concentration plotted against time for all participants. Figure 2 shows the plasma concentrations plotted against time for the poor metabolisers. Figure 3 shows the graph of plasma concentration plotted against time for all other participants on the active treatment. Figure 4 shows the graph plotting plasma concentration against time of the 2 placebo-controlled participants.
Graphs plotting plasma concentration against time for each individual trial participant can be seen in Appendix 1 (not included).
Figure 5 shows the requested PK data for all active participants to two decimal places. Poor metabolising outliers have been analysed separately. Figure 6 shows the Means and Standard Deviations to decimal places. Figure 7 shows the Median and Interquartile values and range for Tmax.
The area under the curve (AUC) has been calculated as 0-infiinty. This was chosen as it better represents the Pharmacokinetics (PK) of the drug in question and provide a greater understanding of the likely exposure. However, this calculation for the outlying, poor metabolisers is not ideal. The total AUC of the extended area is 40% and 46%. This is well outside the reasonable limit of 20% and indicates that the true PK parameters for theses participants is likely to be quite different.
Data was plotted on a conventional graph, the elimination phase was identified as the period preceding a plasma concentration of zero. This is because although the data collected suggests no presence of the drug, we cannot be sure at which time point this actually occurred at. Time periods increased in length as the trial progressed. Logarithmic calculations were then carried out on the raw data in order to determine Kelim and project the terminal phase slope to zero measurable/detectable concentration level.
Appendix 2 (not included) shows the complete data tables and calculation results. Appendix 3 (not included) shows the formula selected to calculate the required figures.
As we can see that two of the participants have nil plasma concentrations at all time points. This would indicate that these two individuals (6 & 7) were administered placebo or no active drug explaining why they do not show any concentration of the drug. Participants 5 & 11’s PK profiles are quite different from the rest of the active participants. They show a much slower elimination, a delayed Tmax and higher Cmax. These individuals can be characterised as poor metabolisers. As such, they experience a dramatically different Half-life (T ½) and their overall systemic exposure is significantly higher than the other active participants. Furthermore, two out of ten participants represent a sizable fraction of the participant population and this would certainly warrant further investigation before moving into larger sample sizes and further stages of clinical development.
When the PK values are analysed by Log, we see that negative values are generated (Appendix 2). While this might seem problematic, the nature of Log values are such that any decimal value less than 1 (i.e. 0.x) will generate a negative Log and in fact, this PK data is valuable when calculating the Kelim as this provides another figure that can be used to determine the slope of the line and therefore the rate of elimination
Figure 1 – Graph showing plasma concentration (μg/ml) against time (hrs) for all research participants
Figure 2 – Graph showing plasma concentration (μg/ml) against time (hrs) for poor metabolising participants (subjects 5 & 11)
Figure 3 – Graph showing plasma concentration (μg/ml) against time (hrs) for normal metabolising participants administered the active drug (subjects 1, 2, 3, 4, 8, 9, 10 and 11)
Figure 4 – Graph showing plasma concentration (μg/ml) against time (hrs) for participants placebo administered participants (Subjects 6 and 7)
Figure 6 – Table to show Means and Standard Deviations to decimal places.
Figure 7 – Table to show Median and Interquartile Tmax values.
Discuss the potential side effect problems that might be encountered with this compound.
Adverse effects associated with use of macrolide antibiotics are well established, a recent Cochrane review looked at over 180 individual studies and found that incidence of gastrointestinal problems associated with macrolides was high (Hansen et al., 2019). Interestingly, they did not find any evidence to support the increase in cardiac issues. Macrolide impact on QT prolongation and Torsades de pointes have been previously identified (Yap, 2003). The Cochrane review did also not find any significant impact of macrolides on hepatic function which is counter the established literature whereby hepatic impact of macrolides is well determined (LiverTox, 2017)
The macrolide class effect of inhibiting CYP3A4 (Lindstrom, Hanssen and Wrighton, 1993) is of concern as this is highlighted as being the mode of action of around 30% of all prescribed drugs as well as many OTC drug such as, acetaminophen (Westphal, 2001). Interactions with statins have also been shown to increase lead to rhabdomyolysis, renal injury and an increase in mortality (Abu Mellal, Hussain and Said, 2019) which is, clearly, a serious set of considerations. Especially so, when the poor metabolisers from the clinical trial are considered as the toxic effects of the drug are likely to be of dramatically higher significance for those individuals with greater system exposure, due to their slow metabolism of the drug. The trial data shows a mean half-life of the drug at 0.67 hours, compared to 5.6 and 4.6 hours respectively for the poor metabolisers, indicating over 5x greater half-life.
Macrolides also exhibit enterophepatic recycling and are eliminated through the biliary route. This has been shown to cause nausea symptoms as a result of product accumulation. This mechanism has been implicated in the development of hypertrophic pyloric stenosis in infants (Honein et al., 1999). Further, metabolic cholestasis, the disruption of bile formation has also been highlighted as potential side effects of macrolide antibiotics (Padda et al., 2011).
How would the PK data presented here influence your future development of the compound as an oral antibiotic for widespread use in general practice?
The presence of poor metabolisers in a large fraction of the trial population indicates that the effects outlined in the previous section warrant much attention. Equally due to the genetic factors of poor metabolism, there are likely to be patients who are rapid metabolisers and this will influence dosing recommendations. It may therefore be prudent, although not necessarily feasible, that patients are pharmacogenomically screened as genetic heterogenicity in clinical populations can account for huge differences in drug efficacy and toxicity (Lanham, 2010).
As this drug is intended for widespread use in general practice, drug interaction with commonly prescribed medication such as statins must be investigated further and the route of metabolism of the drug must be established. in-vitro assays and screens to determine which liver enzymes are responsible for drug metabolism are critical. Different macrolides inhibit CYP3A4 to varying degrees (von Rosenstiel and Adam, 1995). Macrolides have been indicated to interact with a wide range of drug classes from Benzodiazepines and warfarin through to antihistamines and immunosuppressants (Westphal, 2001). Depending on results from these additional studies the drug may require reformulation to ensure it has minimal interaction with liver enzymes, for example, azithromycin and diritromycin have significantly lower interaction with CYP3A4 compared to other macrolide drugs (von Rosenstiel and Adam, 1995). Drug-interactions may also warrant contraindications for the use of this macrolide antibiotic in combination with widely prescribed or administered drugs. Without seeing data on the drug’s pharmacologically active range, it is difficult to determine its suitability for widespread, general use. Drugs that are likely to be self-administered in this way must have very wide therapeutic windows. The literature demonstrates where therapeutic windows are narrow, drug related problems are significantly more likely to occur (Blix et al., 2010).
Properties like the in-vivo target affinity in-vitro bioassays can be used to predict the likely drug-target affinity and is extremely important to collect. Further PK-PD modelling work should be carried out, including determination of the minimum inhibitory concentration in order to determine the dose-effect range in order to identify the Optimal biological dose make appropriate recommendations to prescribers and patients. This is especially important given the highlighted adverse effects and prevalence of poor metabolisers in the patient population. Micro-dialysis can be used to determine the unbound drug concentration in the interstitial fluid, often the target for infections. This analysis would be more useful in determining drug efficacy than the plasma concentrations (Liu, Muller and Derendorf, 2002). Bacterial time-kill curves can provide further insight into the drug efficacy (Brunner, Derendorf and Muller, 2005).
The macrolide erythromycin is shown to be a gastrointestinal (GI) prokinetic, this may be the underlying cause of GI discomfort, often cited as a macrolide side effect (Hansen et al., 2019). Galligan and Vanner (2005) describe how acidic environments cause erythromycin to readily degrade and that drug metabolites lead to GI discomfort and toxicity. It should therefore be considered if the drug formulation can include a gastric-resistant, enteric film coating to try and avoid this degradation and reduce the presence or problematic metabolites. A slow-releasing drug formulation may also help to reduce the impact on the GI system and might be considered for the development of this drug (Wen, Jung and Li, 2015).
Some literature promotes the use of herbal extracts to act as hepatoprotectives and nephroprotectives (Tatiya et al., 2012, Kanna, Hiremath and Unger, 2015). While this is area of study is not widely used in clinical practice, it may be worth exploring if these products can indeed be demonstrated to modulate the potential for toxic effects if included in the drug formulation.
Abu Mellal, A., Hussain, N. and Said, A. (2019). The clinical significance of statins-macrolides interaction: comprehensive review of in vivo studies, case reports, and population studies</p>. Therapeutics and Clinical Risk Management, Volume 15, pp.921-936.
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An assignment by: Darren Wogman MSc. Completed as part of Pharmaceutical Medicine MSc at King's College London