1 Introduction
Despite significant progress in biomedical research, which led to a greater understanding of the molecular mechanisms of carcinogenesis and cancer growth, the rate of success in oncology drug development remains inferior to that of other therapeutic fields [
1]. The PI3K–AKT–mTOR signaling pathway plays a critical role in cell growth, survival, and metabolism and is abnormally expressed in many types of cancers [
2]. Despite significant resources invested in developing PI3K-targeted drugs, the number of approved inhibitors remains limited. Many trials evaluating these drugs have failed at various stages of clinical development [
3] or even post-approval [
4], making it necessary to improve the dose-finding and dose-optimization approach to maximize both safety and efficacy when targeting this pathway.
Dose-finding trials for cytotoxic chemotherapy drugs used to treat cancer have historically been designed to determine the maximum tolerated dose (MTD). In contrast, most modern oncology drugs are targeted therapies, such as targeted kinase inhibitors, which are designed to modulate specific targets with the resulting effect of suppressing molecular pathway(s) driving an oncologic disease [
5]. Hence, these targeted therapies are considered “safer” compared with commonly used cytotoxic drugs and can often achieve maximal therapeutic effects at doses well below the MTD [
5]. As a result, the Food and Drug Administration (FDA) created the Project Optimizing Patient Treatment through Integrative Multiomics Use (OPTIMUS), which is a research initiative aimed at developing new methods to use multiomics data (such as genomics, proteomics, and metabolomics) to improve the selection and dosing of cancer therapies [
6]. The goal of the FDA project is to incorporate new biomarkers that can be used to predict how a patient will respond to a particular treatment and to move forward with a dose-finding and dose-optimization paradigm across oncology that emphasizes selection of a dose or doses that maximize not only the efficacy of a drug but its safety and tolerability, as well [
6]. Quantitative integrated analyses that bridge preclinical research and clinical trials play a crucial role in regulatory decision-making, offering supportive evidence of efficacy when optimizing therapeutic outcomes.
Integrated pharmacokinetic (PK)–pharmacodynamic (PD)–efficacy models are mathematical models that integrate information from pharmacokinetics, the pharmacodynamic biomarker, and efficacy to predict the relationship between drug concentration, target modulation, and the therapeutic response [
7,
8]. The PK portion of these models describes how the body absorbs, metabolizes, distributes, and excretes a drug to provide information on how the drug concentration changes over time in the body. The PD portion of these models provides information on how the drug concentration alters biological target modulation, for example, the inhibition of a target protein. The efficacy portion of these models describes the relationship between the biological target modulation and the therapeutic outcome, for example, how the drug’s effects on the target protein translate into treatment outcomes, such as tumor shrinkage. Integrated PK–PD–efficacy models are important tools in translational drug development to bridge preclinical and clinical studies, as they allow researchers to predict the efficacious dose and to optimize dosing regimens. Despite the increased use of translational PK–PD modeling, there are few to no reported cases of a systematic examination of performance of translational PK–PD modeling using preclinical data on predicting patient response. In this study, we retrospectively employ an integrated PK–PD–efficacy-model approach to explore the exposure–response relationship of apitolisib in both preclinical and initial clinical phases to investigate relationships between drug exposure, biomarker modulation, and efficacy for apitolisib in xenograft mice and patients with cancer in phase 1 trials.
Phosphorylated Akt (pAkt) is a key PD biomarker in the PI3K–AKT–mTOR pathway, which is frequently dysregulated and hyperactivated in cancer cells [
9]. Akt phosphorylates and activates downstream targets involved in cell survival, proliferation, and protein synthesis. For instance, it can activate the mammalian target of rapamycin (mTOR), a critical regulator of protein synthesis and cell growth [
2]. The detection and measurement of pAkt levels serve as a biomarker to assess the activation status of this pathway [
10,
11]. Apitolisib is a potent, selective dual inhibitor of PI3K and mTOR. Apitolisib has broad activity in xenograft efficacy tumor models [
12], which is also demonstrated by collected pAkt protein. Clinical studies, reported previously by Dolly et al., have further established the PD evidence of apitolisib target modulation. Significant reductions in PI3K inhibition of ≥ 90% was observed in platelet-rich plasma PD markers, including pAkt at doses ≥ 16 mg in patients [
13].
Dose optimization of targeted drugs can be informed by translational pharmacodynamic biomarkers that aim to define the degree of target and pathway modulation required for efficacy [
10]. The suitability of these PD biomarkers is typically assessed and characterized at preclinical stages of drug development, providing an understanding of the relationship between drug concentrations and biomarker response as well as the biological relationship between modulation of the biomarker and downstream therapeutic effect. Often, in oncology, preclinical information regarding PD biomarker modulation is obtained from tumor tissue (site of action). Tumor tissue biomarkers provide a direct look at the extent and duration of target modulation. In contrast, in the clinic, collection of plasma biomarkers is far more common and serves as a surrogate of target modulation in a tumor. In this study, we aim to (1) develop an integrated PK–PD–efficacy model in xenograft mice to characterize both apitolisib’s ability to modulate pAkt in tumor tissue and to inhibit tumor growth; (2) utilize the same modeling methodology to characterize apitolisib PK, surrogate PD (pAkt modulation/response) in plasma, and efficacy (tumor growth inhibition) in patients with cancer in phase 1 clinical trials; and (3) evaluate the translatability of the preclinical analysis in xenografts to observations in patients with cancer. These objectives provide information that will enhance the value of future clinical and preclinical translational dose-finding and dose-optimization studies.
4 Discussion
The PI3K–AKT–mTOR signaling pathway is essential for numerous fundamental cellular functions such as cell differentiation, growth, proliferation, mobility, and metabolism [
2,
10]. Somatic alterations and genetic amplifications causing activation of this pathway are frequently found in cancer; as a result, there has been a significant effort to develop therapeutics that target critical members of the pathway [
10]. Successful development of these drugs requires pharmacodynamic biomarkers that aim to define the degree and duration of target and pathway inhibition [
10], such as pAkt (phosphorylated Akt), which is a protein biomarker that is often used as a marker of cellular signaling through the PI3K–AKT–mTOR pathway. Drugs that target this pathway, such as apitolisib, lead to the inhibition of pAkt phosphorylation and the induction of apoptosis in cancer cells [
12]. In this study, preclinical and clinical apitolisib concentration–response relationships were explored to investigate the utility of the pAkt biomarker in assessing the extent of PI3K–AKT–mTOR pathway inhibition in terms of tumor growth inhibition. More importantly, we present a case study of a formal assessment on the real-world translatability of preclinical translational PK–PD analyses to clinical response in patients using common endpoints that are collected at the preclinical and early clinical stages of drug development (refer to Supplementary Information, Online Resource 1: Analysis objectives). Formal assessment on the translatability of preclinical PK–PD is lacking in literature.
Levels of the biomarker, pAkt, can be measured in both tumor tissue and plasma. However, measurement of pAkt in the tumor tissue is typically considered more relevant to therapy and serves to better inform cancer prognosis. Development of targeted therapeutics for cancer at the preclinical research stage is conducted on the xenograft models to determine drug efficacy and identify active doses based on assessment of the drug’s in vivo concentration and target engagement at the site of action. At this stage, the extent and duration of target modulation typically is quantified by measuring tumor tissue biomarkers. However, once the drug moves into the clinical stage, often target modulation and response is assessed by surrogate plasma biomarkers, which are less invasive.
PD biomarkers provide invaluable insights into the interaction of novel therapies with their intended targets; however, use of these biomarkers requires a deep understanding of their relationship to drug concentrations and clinical outcomes. Over the past few decades, there has been a significant rise in preclinical understanding of drug concentrations and biomarker response as evident in numerous publications [
20‐
22]. This development is driven by advancements in technologies and methodologies for measuring drug levels and biomarkers in various biological matrices. One of the key factors driving the interest in preclinical understanding of drug concentrations and biomarker response is the need to improve the efficacy and safety of drugs in clinical development and design more effective dosing regimens that minimize the risk of adverse events. Based on our knowledge, this is the first report to quantitatively examine the relationship between target modulation and efficacy in humans for a PI3K compound using a translational biomarker (pAkt). Additionally, this is the first attempt to quantitatively assess similarities and differences between the biomarker response observed in the preclinical and clinical phases to assess the translatability of preclinical PD and efficacy data within the PI3K–AKT–mTOR pathway. In this study, we focused on a translational PD biomarker in the pathway, pAkt, and developed an integrated sequential PK–PD–efficacy model to facilitate the interpretation of efficacy studies conducted from the preclinical to early phases of clinical development for a specific PI3K/mTOR inhibitor compound (apitolisib). These integrated models can be used to predict the optimal dosing regimen for a drug, considering not only the PK properties of the drug but also the relationship between drug concentrations, pAkt levels, and tumor growth inhibition.
The model used to characterize the relationship between apitolisib and pAkt levels is an indirect response model. Indirect response models are used when the response measured is the product of an indirect mechanism, such as the inhibition or stimulation of the formation (
kin) or loss (
kout) of the mediator controlling the physiological effect [
23,
24]. Hence, in the present study, the levels of pAkt in tumor tissue (preclinically) and plasma (clinically) that were monitored after upstream inhibition of PI3K by apitolisib were characterized using indirect response models to quantify the inhibition of the production of pAkt in tumor tissue or surrogate plasma. The quantified
kin estimates were comparable between xenograft mice and humans, suggesting comparable rates of production and loss (
kout is derived from
kin) for pAkt (Tables
1 and
2) in surrogate plasma from patients versus tumors from xenograft mice. Notably, the estimated IC
50 was ~40-fold lower in a typical patient, being 9.32 µg/L compared with 403 µg/L in xenografts when the PD biomarker was assessed in surrogate plasma rather than from tumor tissue. The higher pAkt IC
50 estimates in xenograft study could be partially due to the RCC tumor type (786-O cell line) being less responsive to apitolisib treatment compared with other tumor types such as MCF-7 in in vitro studies conducted using cancer cell lines [
12,
25]. In patients in whom the plasma pAkt biomarker was collected, individual IC
50 ranged from 4.03 to 26.1 µg/L (Supplementary Table S1); notably, within this group, the individual IC
50 estimate for a patient with metastatic renal cell carcinoma was 19.12 µg/L, which was on the higher end.
The higher potency observed in patient surrogate plasma may be attributed to differences in assay conditions that likely favor greater inhibition in the plasma or differences in drug concentrations between normal and tumor tissues due to variances in tissue architecture, and hemodynamics may also contribute to this phenomenon [
26]. These differences underline the complexity of accurately defining the pharmacokinetic–pharmacodynamic relationship of molecularly targeted drugs based on observed data alone, as well as the important issues of extrapolation of preclinical models to predict effects in patients and differences in analytical sensitivity/methodology between platelet-rich plasma and tumor biopsies [
26]. Model-informed drug development can identify and characterize these differences and their correlation to treatment effect; therefore, disease-mechanism-based biomarkers that are indicative of a relevant biological aspect of cancer can serve as additional surrogates of antitumor activity during the drug-development process and bridge preclinical studies efficacy and clinical response.
The relationship between pAkt modulation and its impact on tumor growth was investigated preclinically and clinically by developing integrated PK–PD–efficacy models that assume that tumor growth inhibition has dependence on the PI3K–AKT–mTOR signaling pathway (Supplementary Fig. S1). Apitolisib is a dual PI3K/mTOR inhibitor that targets two crucial points in the same pathway that could lead to higher efficacy compared with a PI3K inhibitor alone by possibly overcoming mTOR negative feedback due to inhibition of pathway [
27]. In our current integrated models used for both the preclinical and clinical analyses, pAkt inhibition serves as surrogate for both PI3K and mTORC1 inhibition by apitolisib; hence, the models utilized have some limitations. Specifically, the role/contribution of mTORC2 inhibition to tumor growth inhibition is not accounted for separately and is “lumped” in with PI3Kinase inhibition by the models used. Further, preclinical study was conducted in xenografts with renal cancer carcinoma (786-O cell lines), and relationships between pAkt modulation and tumor inhibition may differ when compared with patients with different cancer types enrolled in the phase 1 studies utilized in this analysis.
Individual longitudinal tumor profiles were adequately described by both preclinical and clinical integrated PK–PD–efficacy models. Numerical and graphical model diagnostics were used to evaluate different components of the integrated PK–PD–efficacy models, which verified adequacy of the underlying structural models to capture observed preclinical and clinical data. A steep sigmoid curve describing the relationship between inhibition of Akt phosphorylation and tumor growth inhibition, represented by the rate constant
Ks, was observed in xenograft mice and patients despite differences in species and matrices where pAkt inhibition was evaluated. In both species, a threshold of approximately 35–45% pAkt inhibition is required prior to rapid increases in tumor growth inhibition with increasing pAkt modulation (Fig.
2). The commonality of curve shape has implications in clinical trial design, as pharmacodynamic endpoints can be appropriately selected in clinical trials based on similarity in observed pharmacodynamic thresholds required prior to onset of an antitumor effect. A similar steep relationship (sigmoidicity factor ~9) was estimated for target modulation (Gli mRNA inhibition) and vismodegib efficacy (i.e., the rate constant describing the measure of antitumor effect) in the preclinical models of the hedgehog pathway, and approximately a minimum of 50% Gli mRNA inhibition was needed for tumor growth inhibition [
28]. Finally, of note, the net growth rate constant was considerably lower in patients compared with xenograft mice. This is consistent with previous publications of the tumor growth rate constant in humans versus preclinical xenograft models [
28,
29].
Previous study delves into the translational relevance of preclinical murine subcutaneous tumor models in predicting clinical response [
30]. Through a retrospective pharmacokinetic–pharmacodynamic analysis of eight agents, including both molecularly targeted and cytotoxic ones, with known clinical response data, the research sheds light on the correlation between simulated tumor growth inhibition in xenografts/allografts using human exposures and actual clinical response. A significant correlation was found between tumor growth inhibition (TGI) driven by human pharmacokinetics and clinical response, contrasting with TGI observed at maximum tolerated doses in mice. Agents inducing over 60% TGI in preclinical models, at clinically relevant exposures, exhibited a higher likelihood of eliciting clinical response. These findings establish a framework for effectively utilizing murine subcutaneous tumor models for selecting promising agents for clinical advancement.
In summary, modeling can play a critical role in bridging the gap between xenograft and human studies and in facilitating the development of more effective cancer treatments. The complexity of accurately defining the treatment exposure–response relationship based on different matrices of origin (tumor tissue versus surrogate plasma) due to analytical sensitivity and methodology is challenging, specifically at lower drug exposures. The preclinically and clinically developed integrated PK–PD–efficacy models bring together the information from PK, PD, and efficacy studies to provide a more complete understanding of the relationship between drug dose, drug concentration, and therapeutic response. The primary objective of our analysis was to examine a real-world translational analysis and assess both insights gained and shortcomings of the analysis. The overall goal of this analysis was to better characterize preclinical to clinical translation, with the aim of improving predictivity and use of translational biomarkers such as pAkt, which reflect the biological response of a drug. These PD biomarkers can be used to monitor the treatment efficacy and facilitate informed decision-making on dose finding and dosing-regimen optimization, to better inform future clinical trial design and interpretation based on preclinical studies.