Apocalyptic clinical pharmacology helps drug developers save resources and time through providing a framework for understanding the interaction of the drug under development with the biological system. This framework also helps answer questions from regulatory authorities who are evaluating the safety and efficacy of the drug. Making informed decisions throughout the drug development process requires having the appropriate data and information about the drug. This blog will focus on several case studies where using apocalyptic clinical pharmacology helped several companies identify information about their drug and key relationships and ultimately, how to proceed with their drugs in the market.
Case 1: Post-commercialization example—Treatment of prostate cancer
This example used apocalyptic clinical pharmacology to uncover the appropriate target testosterone concentration range in treating prostate cancer. The client wanted to know if the drug’s response target, whose administration results in pharmacological castration, was appropriate to achieve optimized benefit/risk. To identify whether the observed testosterone concentration led to the best outcomes, several relationships needed to be uncovered.
Here are the relationships that were of interest:
- What is the relationship between dose and pharmacokinetics (PK)?
- What’s the relationship between the PK and the testosterone response?
- What is the relationship between testosterone response and prostate specific antigen (PSA) response?
- How does all of this impact the clinical outcome?
1. Relationship of dose to PK: Using existing data, describing the relationship of drug concentrations with respect to time and with respect to the dose, to the PK helped the company understand and describe this first relationship. This step was straightforward for this particular drug, but as other examples in this blog will show, sometimes this step is more complicated.
2. Relationship of PK to testosterone: This was the key to the development question. Plots of the concentration of testosterone versus time showed when and for how long the conventionally accepted testosterone concentration target was achieved after the drug was administered. Descriptions of the relationship of PK to testosterone established the target plasma drug concentrations required to hit the conventionally accepted testosterone target suppression. Plots showed that in 95% of the subjects at that particular concentration of the drug, this target testosterone concentration was reached. Thus, through understanding the details of the relationships that underpin the overall relationship between dose and efficacy the client was able to have greater insight into how these relationships impact the desired target level of suppression of testosterone.
3. Relationship of testosterone to PSA: This step was important to address the question as to whether conventionally accepted testosterone target suppression was optimal; it is further along the pathway to the ultimate clinical outcome. Often with the pressures of timelines, it is difficult for drug developers to take the time to understand each of these relationships. The relationship of testosterone concentration to PSA suppression demonstrated that there was a point at which further suppression of testosterone has no further impact in lowering PSA. This important new appreciation of this relationship allowed the client to identify a better target range for suppression which is believed to avoid over suppression (and consequential risk of the effects of low testosterone) whilst maintaining a desired impact on PSA.
4. Relationship of PSA level to clinical outcomes: We used a database of longitudinal data to analyze the relationship of PSA concentration to survival rate. While lower PSA is correlated with increased survival time, it’s not certain at this stage if the clinical target should be fixed to a certain target PSA concentration or whether a delta PSA from baseline is preferable. In any event, by understanding all of these relationships, this approach facilitates optimization of dose to create the best conditions for such treatments.
By understanding these relationships and describing them in a way that drug developers can use the information gained, the developers can make informed decisions about drug dosing and achieving optimal efficacy.
Case 2: Relationship of drug to PK—Drug with highly variable PK
This second example came from a first-in-man study, single rising dose and multiple rising dose that reported high variability in the drug pharmacokinetics. Each cohort had one or two subjects that had three- or four-times higher exposure than the rest of the group—this presented some development issues given the therapeutic margin of the drug.
To understand this dataset, we generated a frequency plot of dose normalized AUC. This revealed a bi-modal distribution of exposure. Thus, having uncovered this phenomenon, the team wanted to understand the underlying mechanism in the hope that this might facilitate the development of a strategy to mitigate the impact. Like the first example, this example focused on the relationship between the drug dose and PK and efforts to understand how different aspects in the system influence drug exposure.
Through analysis of the drug’s metabolic pathways, the team narrowed down the pathway and an enzyme genotype that was impaired in a subpopulation. After much analysis, the team overlaid the bi-modal distribution with the genotype data and observed a near-perfect alignment between the individuals with a specific genotype and higher exposure to the drug. The team identified three strategies to proceed with the development:
- Justify that the drug safety profile is adequate and allow all patients receive the same dose irrespective of phenotype. If the safety margin was sufficiently wide, higher exposure and drug concentrations could be justified in the subpopulation of poor metabolizers.
- Titrate and measure the impact of the drug treatment on the clinical marker for the disease, start at a low dose, and then increase the dose. Thus, patients with the poor metabolizer phenotype would receive a lower dose than the rest of the population.
- Genotype patients to decide prospectively the appropriate dose for each patient.
This drug program was terminated for other reasons, so the development strategy did not ultimately need to be implemented. But this example illustrated how to understand the interplay of the drug and the system in which it’s placed, using an apocalyptic approach to uncover key relationships. As drug developers understand those relationships, they can make better decisions about whether the drug is viable, but also better decisions about how to optimize the drug for specific patient populations.
Case 3: Drug-drug interactions (DDIs): Relationship of drug to PK/PD—Impact of the system on the drug
This example focused on the impact of the system on the drug itself. The anti-cancer drug, ibrutinib, is a CYP3A substrate. Thus, the developers were concerned about potential DDIs with co-administration of CYP3A inhibitors or inducers. They performed a conventional DDI study with a strong inhibitor of CYP3A (ketoconazole). They observed in their clinical trial a 27-fold elevation in ibrutinib’s AUC ratio. In another DDI study, they co-administered rifampicin, a CYP3A inducer, and they observed more than 90% reduction in the AUC of ibrutinib. Thus indicating that ibrutinib is a victim of substantial CYP3A-mediated DDIs.
If you remember my blog on apocalyptic clinical pharmacology, the point of this framework is that there are not enough resources or time to perform every clinical study. Drug developers need to choose the studies that will give the most information about the underlying relationships and fill in the gaps with modeling approaches. The DDI studies already performed quantified the magnitude of the change in ibrutinib exposure due to co-administration of a strong CYP3 inhibitor and inducer. The drug developers next uncovered what could be anticipated between the extremes of these two scenarios.
Simulated and observed Ibrutinib AUC ratios with 95% confidence intervals of weak, moderate, and strong inhibitors and moderate and strong inducers of CYP3A4.
Source: US FDA Clinical Pharmacology and Biopharmaceutics Review—Imbruvica® (www.accessdata.fda.gov/drugsatfda_docs/)
They used a physiologically-based pharmacokinetic (PBPK) modeling approach to simulate the ketoconazole and rifampicin DDIs for ibrutinib. The predicted AUC ratio changes from the PBPK model were comparable to the results from the two clinical studies. Then, they used this model to simulate the interactions between ibrutinib and other perpetrators of CYP3A-mediated DDIs (such as erythromycin, diltiazem, and fluvoxamine—moderate inhibitors and efavirenz—a moderate inducer) to fill in the gaps. No further clinical trials were done. This work enabled the generation of useful DDI data that was included in the label with the agreement of regulatory agencies.
This accomplishment is exciting because it means that we are able to use modeling and simulation to provide critical and useful information efficiently. These kinds of approaches can be used to reduce the cost and improve the efficiency of drug development.
The take home of this example is that the drug developers uncovered more information about the key relationship of the drug and the system it interacts with by using simulation studies. Ultimately, they efficiently provided a range clinically relevant DDI recommendations on the basis of two clinical DDIs studies.
To learn more about how to maximize the use of apocalyptic clinical pharmacology in drug development, please watch this webinar I presented on this topic.