Therapeutic Window–Guided Regulatory Extrapolation: A Pharmacokinetic and Pharmacodynamic Framework for Deciding When Local Studies Are Necessary

Therapeutic Window–Guided Regulatory Extrapolation: A Pharmacokinetic and Pharmacodynamic Framework for Deciding When Local Studies Are Necessary | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 1 March 2026 V1 Latest version Share on Therapeutic Window–Guided Regulatory Extrapolation: A Pharmacokinetic and Pharmacodynamic Framework for Deciding When Local Studies Are Necessary Authors : Aldo Iván García-Moncayo and Gilberto Castañeda-Hernández [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177238089.98460793/v1 178 views 114 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The global development of medicines increasingly contrasts with predominantly national regulatory requirements, often leading to duplicated studies and delayed access to effective therapies. While pharmacokinetic variability across populations is frequently invoked to justify local confirmatory trials, the clinical relevance of such variability depends on whether exposure differences meaningfully alter benefit–risk balance at approved doses. This review proposes a therapeutic window–guided framework integrating pharmacokinetic and pharmacodynamic principles to inform regulatory extrapolation decisions. Rather than treating ethnic sensitivity as a geographic attribute, variability should be assessed mechanistically: exposure shifts become clinically relevant only when they plausibly move patients outside established therapeutic boundaries. Using case-based analyses relevant to Mexican regulatory contexts—nifedipine, sildenafil, and vonoprazan—we illustrate how similar magnitudes of pharmacokinetic variability can carry different regulatory implications depending on exposure–response relationships and therapeutic window width. For drugs with shallow or plateaued exposure–response curves and broad functional therapeutic ranges, foreign efficacy data may be acceptable with targeted pharmacokinetic evaluation or labeling adjustments. Conversely, when mechanistic plausibility suggests clinically meaningful displacement of exposure, focused local studies may be warranted. This framework aligns with International Council for Harmonisation guidance on bridging and multiregional clinical trials while operationalizing a risk-based approach centered on clinically meaningful uncertainty. By prioritizing local evidence only when pharmacologic and clinical criteria justify it, regulatory systems may reduce unnecessary duplication without compromising patient safety. Therapeutic Window–Guided Regulatory Extrapolation: A Pharmacokinetic and Pharmacodynamic Framework for Deciding When Local Studies Are Necessary Aldo Iván García-Moncayo 1 , Gilberto Castañeda-Hernández 1* Running title: Therapeutic Window and Extrapolation 1. Department of Pharmacology of Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), Mexico City, Mexico. *Correspondence: Gilberto Castañeda-Hernández ( [email protected] ) Abstract The global development of medicines increasingly contrasts with predominantly national regulatory requirements, often leading to duplicated studies and delayed access to effective therapies. While pharmacokinetic variability across populations is frequently invoked to justify local confirmatory trials, the clinical relevance of such variability depends on whether exposure differences meaningfully alter benefit–risk balance at approved doses. This review proposes a therapeutic window–guided framework integrating pharmacokinetic and pharmacodynamic principles to inform regulatory extrapolation decisions. Rather than treating ethnic sensitivity as a geographic attribute, variability should be assessed mechanistically: exposure shifts become clinically relevant only when they plausibly move patients outside established therapeutic boundaries. Using case-based analyses relevant to Mexican regulatory contexts—nifedipine, sildenafil, and vonoprazan—we illustrate how similar magnitudes of pharmacokinetic variability can carry different regulatory implications depending on exposure–response relationships and therapeutic window width. For drugs with shallow or plateaued exposure–response curves and broad functional therapeutic ranges, foreign efficacy data may be acceptable with targeted pharmacokinetic evaluation or labeling adjustments. Conversely, when mechanistic plausibility suggests clinically meaningful displacement of exposure, focused local studies may be warranted. This framework aligns with International Council for Harmonisation guidance on bridging and multiregional clinical trials while operationalizing a risk-based approach centered on clinically meaningful uncertainty. By prioritizing local evidence only when pharmacologic and clinical criteria justify it, regulatory systems may reduce unnecessary duplication without compromising patient safety. Keywords: Regulatory extrapolation, Therapeutic window, Pharmacokinetic variability, Pharmacodynamic response, Ethnic sensitivity, Multiregional clinical trials, Pharmacoepidemiology Key Points • Pharmacokinetic differences across populations do not automatically justify local confirmatory trials; their relevance depends on therapeutic window boundaries. • A mechanistic framework integrating pharmacokinetics, pharmacodynamics, and therapeutic window width provides a structured basis for regulatory extrapolation decisions. • Exposure shifts are clinically meaningful only when they plausibly move patients outside established benefit–risk limits. • Case examples demonstrate that similar magnitudes of variability can lead to different regulatory implications depending on exposure–response characteristics. • Risk-based regulation should prioritize targeted local evidence only when clinically meaningful residual uncertainty remains. Plain Language Summary The development of medicines increasingly takes place on a global scale, yet regulatory approval is often granted country by country. This can lead to repeated studies, delays in patient access, and unnecessary use of research resources. However, real differences in how people respond to medicines may exist, and regulators must ensure treatments are both safe and effective for local populations. This article proposes a practical framework to guide when additional local studies are truly needed. Instead of focusing only on whether drug levels differ between populations, the key question is whether those differences are likely to change clinical outcomes. We explain that exposure differences matter only if they move patients outside a range where benefits clearly outweigh risks. Using examples of commonly used medicines, we show that similar differences in drug levels can have very different clinical implications depending on how the drug works and how wide its safety margin is. A risk-based approach, grounded in pharmacological principles, can help avoid unnecessary duplication while maintaining patient protection. This strategy supports more efficient regulatory decisions without compromising safety. Statement of prior presentation and funding This work was conducted as part of academic research activities at Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional. It has not been previously published or posted as a preprint. No specific external funding was received. Ethics Statement This article is a conceptual and literature-based review. It does not involve human participants, patient-level data, or identifiable personal information. Therefore, institutional review board approval and informed consent were not required. Conflict of Interest Disclosure The authors declare no conflicts of interest related to this work. INTRODUCTION Global drug development has become the norm for both small molecules and biologics, yet evidence requirements remain largely national. This mismatch can generate duplicated studies, delayed approvals, and uneven access to effective therapies, especially in settings with limited regulatory and research infrastructure (Handoo et al., 2012; Valverde, 2016; Wilson et al., 2020). At the same time, genuine population differences in exposure or response can occur and must be anticipated to protect patients. The practical problem is not whether variability exists (it always does), but whether the magnitude and mechanism of variability are likely to be clinically meaningful at approved doses, and therefore whether local studies add material value beyond global evidence. The International Council for Harmonisation (ICH) attempted to formalize this question through ICH E5, which introduced the concepts of intrinsic and extrinsic ethnic factors and proposed “bridging” strategies to support the acceptability of foreign clinical data (ICH, 1998). More recently, ICH E17 advanced a prospective “global-first” approach through multiregional clinical trials (MRCTs), emphasizing planning and analysis strategies that allow inference across regions while explicitly addressing potential sources of heterogeneity (ICH, 2017; Asano et al., 2021). Although these guidelines provide a regulatory scaffold, the decision to require local studies often remains inconsistently operationalized, in part because “ethnic sensitivity” is sometimes treated as a geographic attribute rather than a mechanistic hypothesis that can be tested and managed (Liu & Chow, 2002; Gupta, 2011). This review argues that regulatory extrapolation decisions are most defensible when anchored in an integrated pharmacokinetic (PK), pharmacodynamic (PD), and therapeutic window framework. PK identifies how a dose translates into exposure and how exposure varies with intrinsic and extrinsic factors. PD describes how exposure translates into benefit and harm, including the shape and steepness of exposure–response relationships (Holford & Sheiner, 1981; Danhof et al., 2007). The therapeutic window then provides an actionable boundary condition: variability matters when it plausibly shifts a meaningful fraction of patients outside exposures associated with acceptable benefit–risk (AbuMahfouz et al., 2017; Bonnet, 2024). Mexico represents a middle-income regulatory setting where balancing access and duplication is particularly relevant, making it an illustrative case for broader global application. Therefore, using case-based examples relevant to Mexican regulatory contexts, nifedipine, sildenafil, and vonoprazan, we illustrate that exposure differences alone do not automatically justify local duplication unless they threaten to alter clinical outcomes by crossing therapeutic boundaries. Drug development timelines remain long and resource intensive. Even under optimistic assumptions, moving a candidate from target validation through clinical phases and regulatory review typically requires more than a decade, with high attrition and escalating expenditures driven largely by late-stage failures (DiMasi, 2020; Singh et al., 2023). To manage this complexity, sponsors increasingly deploy global programs that enroll diverse participants and leverage computational tools to integrate evidence across studies and settings (Singh et al., 2023). However, national agencies still have legal mandates and public expectations to ensure that approved dosing is safe and effective for their populations. In practice, this can lead to additional local requirements even when pivotal evidence exists elsewhere, particularly when agencies anticipate differences in genetics, comorbidities, co-medications, diet, or healthcare delivery that might influence exposure or response (ICH, 1998; Ruckle, 2009). Regulatory heterogeneity is not merely administrative; it has downstream public health implications. If duplicated studies delay access to effective therapies, patients may remain on less effective alternatives, and inequities can widen between regions that can rapidly adopt innovation and those that cannot (Wilson et al., 2020). Moreover, duplication may divert limited clinical research capacity away from locally relevant questions such as adherence, real-world effectiveness, safety in comorbid populations, and implementation barriers. A risk-based approach therefore requires a clear “value of information” argument: what uncertainty is not resolved by foreign data, how likely is it to be clinically important in the target region, and what is the least burdensome evidence strategy to reduce that uncertainty? This logic is consistent with ICH E5/E17 but becomes actionable only when translated into PK/PD and therapeutic window terms. METHODS This review is not a systematic review or meta-analysis; it is a mechanistic and regulatory synthesis intended to clarify decision logic. Therefore, numerical cross-population comparisons should be interpreted cautiously because study designs, formulations, sampling schedules, and analytical methods differ across historical datasets. For this reason, our figures use an empirical exposure reference band (defined by non-Mexican means) to visualize variability rather than to claim a formal therapeutic window. Where possible, extrapolation arguments are supported by mechanistic plausibility and by the consistency of PD performance across settings. Although some cross-population datasets are historical, they remain the primary published comparative PK analyses available and are used here illustratively rather than inferentially The therapeutic window is operationalized here as a clinically informed boundary derived from exposure–response and safety data rather than fixed plasma concentration cutoffs, which are not universally established for these agents. Additionally, the framework does not replace ethical considerations. Even when science suggests limited need for local duplication, community trust, communication, and transparency about uncertainties remain essential. Finally, while the case studies focus on Mexico-relevant examples, the proposed decision algorithm is generalizable to other settings where regulators must balance access and protection under uncertainty. Pharmacokinetic Sources of Variability That Precede Geography Pharmacokinetics describes absorption, distribution, metabolism, and excretion (ADME) and provides quantitative parameters, AUC, Cmax, Tmax, half-life, clearance, and volume of distribution, that summarize exposure over time (Urso et al., 2002; Sandhya et al., 2025). Importantly, the biological sources of PK variability exist within all populations. “Interregional” differences are often reflections of different distributions of the same determinants rather than uniquely regional biology. Absorption variability is particularly relevant for orally administered drugs, where gastrointestinal pH, motility, food effects, formulation differences, and first-pass metabolism can shift bioavailability. First-pass effects can be a major driver of between-study and between-population differences because they amplify small differences in intestinal/hepatic enzyme or transporter activity into larger differences in systemic exposure. Distribution variability depends on tissue perfusion, plasma protein binding, body composition, and physiological barriers. Metabolism is often the dominant source of exposure variability for small molecules, especially via cytochrome P450 enzymes. Excretion variability is driven by renal and hepatic function, age, comorbidities, and transporter activity (Ernstmeyer & Christman, 2023; Verbeeck, 2008). Genetic variability is a central intrinsic determinant. Polymorphisms in CYP enzymes (e.g., CYP2C19, CYP2D6, CYP2C9, CYP3A4/5) and transporters (e.g., ABCB1/P-gp, SLCO/OATPs) can alter clearance and bioavailability, producing different exposure distributions even with identical dosing (Ingelman-Sundberg, 2005; Zanger & Schwab, 2013). However, genetic variability is typically continuous and overlapping across populations. Thus, “ethnic sensitivity” is better conceptualized as the likelihood that a target region has a meaningfully different distribution of PK determinants that would shift exposure beyond clinical thresholds. Extrinsic factors can be equally impactful. Concomitant medications may inhibit or induce enzymes and transporters, producing exposure changes that exceed those attributable to genetic differences alone (Lin & Lu, 1998; Zhou et al., 2007). Diet, herbal products, smoking, alcohol use, and environmental exposures can also modulate metabolism. Clinical factors—hepatic steatosis, chronic kidney disease, inflammatory status—shift clearance and sometimes distribution. These are not unique to any one region, but their prevalence may differ, and that difference can matter more than ancestry categories. For regulators, the key implication is that PK variability should be evaluated mechanistically and quantitatively. If a plausible determinant differs by region and the resulting exposure shift is large relative to the therapeutic window, a targeted PK evaluation or modeling exercise may be warranted. If not, requiring a local efficacy trial simply because “PK differs” is unlikely to be efficient or clinically justified. Pharmacodynamics: When Exposure Differences Matter Pharmacodynamics links exposure to effect. The same exposure difference can be clinically trivial for one drug and consequential for another depending on the exposure–response curve. Holford and Sheiner (1981) described the foundational separation between the “PK problem” (what concentration is achieved) and the “PD problem” (what effect that concentration produces), emphasizing that both must be considered to predict clinical outcomes. Danhof and colleagues later expanded how time delays, receptor dynamics, and system properties shape observed responses and how modeling can translate biomarker changes into clinical expectations (Danhof et al., 2007). Three PD patterns are especially relevant for extrapolation: (1) Steep exposure–response: small exposure changes cause large shifts in efficacy or toxicity, often characteristic of narrow therapeutic index drugs. (2) Plateaued efficacy with toxicity at higher exposures: efficacy saturates at moderate exposures, while adverse effects increase later. Moderate exposure increases may not improve efficacy but can raise risk. (3) Broad plateau for both efficacy and tolerability: wide ranges of exposure produce similar benefit–risk, so exposure differences are often clinically buffered. PD variability also arises from target polymorphisms, receptor expression differences, downstream signaling, disease severity, and competing pathophysiology. Moreover, surrogate endpoints may not perfectly predict patient-relevant outcomes, which complicates inference when regional trials use different endpoints or healthcare contexts (Lesko & Atkinson, 2001). Therefore, extrapolation decisions should focus on whether a plausible exposure shift would meaningfully alter clinical outcomes through a known exposure–response relationship, rather than on statistical differences in PK parameters alone. Therapeutic Window: Terminology and Practical Use The therapeutic window is commonly defined as the range of exposures (or concentrations) that maintain benefit without unacceptable harm (AbuMahfouz et al., 2017; Bonnet, 2024). For regulatory decision-making, the critical feature is not the elegance of the definition but its operational role: it provides boundaries against which exposure distributions can be compared. Because your manuscript uses empirical cross-population exposure comparisons, it is useful to distinguish three related but non-identical concepts: • Classical therapeutic window: boundaries derived from integrated PK, PD, and toxicity/clinical outcome evidence, often codified in labeling, therapeutic drug monitoring targets, or clinical guidelines. • Functional therapeutic window: a pragmatic boundary inferred from consistent clinical performance across a range of exposures observed in trials and practice, even if precise concentration thresholds are not formally established. This is common for drugs where PD endpoints are robust and clinical failure is rare within approved dosing. • Empirical exposure reference band: a descriptive range defined by mean exposure values observed across comparator populations, used only to visualize and contextualize cross-population PK differences. It is not a therapeutic window and should not be labeled as such. Using these terms consistently reduces reviewer confusion. When your figures shade the range formed by non-Mexican mean exposures, that shading should be described as an empirical exposure reference band. The clinical interpretation can then be framed as “clinically tolerated exposure range” or “functional therapeutic window” only if supported by evidence that exposures in that band correspond to acceptable outcomes (Table 1). Table 1. Population-level exposure metrics used to contextualize regulatory extrapolation across nifedipine, sildenafil, and vonoprazan Nifedipine English Oral IR AUC ~145–155 ng·h/mL SEM Within band Wide functional window Foreign data acceptable Castañeda-Hernández et al., 1992. Nifedipine USA Oral IR AUC ~125–145 ng·h/mL SEM Within band Wide functional window No local efficacy duplication Castañeda-Hernández et al., 1992. Nifedipine German Oral IR AUC ~160 ng·h/mL SEM Upper band limit Still within window Dose awareness, not duplication Castañeda-Hernández et al., 1992. Nifedipine Mexican Oral IR AUC ~267–384 ng·h/mL SEM Above band Clinically tolerated PK difference ≠ clinical risk Castañeda-Hernández et al., 1992. Sildenafil White men 100 mg single dose AUC ~1500 ng·h/mL SEM Reference population Wide therapeutic window Global dose acceptable Flores-Murrieta et al., 2000 ; manufacturer data cited therein. Sildenafil Mexican men 100 mg single dose AUC ~3000 ng·h/mL SEM Above band PD plateau reached No need for local trials Flores-Murrieta et al., 2000 Vonoprazan Japanese 10–40 mg, day 7 AUC₀–₂₄ Dose-proportional SD Reference band Wide PD buffer Extrapolable Jenkins et al., 2015; Sugano et al., 2018. Vonoprazan British 10–40 mg, day 7 AUC₀–₂₄ Overlaps Japanese SD Within band Robust acid suppression No efficacy bridging Jenkins et al., 2015; Sugano et al., 2018. Vonoprazan Mexican — — No data — Not evaluated Mechanistically predictable RWE > confirmatory trial — The empirical exposure reference band is defined as the range delimited by mean exposure values observed in Mexican and non-Mexican populations and is used solely to contextualize cross-population exposure variability. It does not represent a formal therapeutic window. From Principles to Regulatory Action: A Decision Algorithm To make PK/PD–therapeutic window logic usable for regulators and reviewers, we propose a stepwise algorithm aligned with ICH E5/E17: Step 1: Define the clinical question and endpoint of concern (efficacy, safety, interaction, long-term outcome) and the intended regimen. Step 2: Identify plausible determinants that differ in prevalence or magnitude in the target region (genotype frequencies, comedications, organ impairment, diet). Step 3: Quantify expected exposure shift using existing PK data, physiologically based PK (PBPK) modeling, population PK analyses, or bridging PK studies. Step 4: Map exposure shifts onto exposure–response knowledge, focusing on whether shifts could move patients outside the classical or functional therapeutic window. Step 5: Select the lowest-burden evidence strategy that resolves the residual uncertainty: targeted PK study, interaction study, post-authorization real-world evidence (RWE), or, only when needed, a local confirmatory trial. This logic supports a “minimum necessary evidence” approach: local trials are justified when the probability-weighted clinical harm from extrapolation is non-trivial and cannot be mitigated by dosing adjustments, labeling, monitoring, or targeted studies (Table 2). Nonetheless, future work could formalize exposure-shift thresholds using PBPK simulations and probability-weighted risk estimation to quantify decision boundaries Table 2. Therapeutic window–guided decision matrix for regulatory extrapolation across case studies Nifedipine Moderate to high (↑ AUC in Mexicans) Shallow Wide Low Low-risk extrapolation Accept foreign efficacy data; consider dose awareness only Sildenafil High (≈2-fold ↑ AUC in Mexicans) Plateaued Wide Very low Robust extrapolation No local efficacy or safety trials justified Vonoprazan Low–moderate (overlapping AUC across regions) Robust PD (rapid, sustained acid suppression) Wide functional window Low–moderate (DDIs, long-term safety) Conditional extrapolation RWE or targeted PK/interaction studies preferred over confirmatory trials PK variability classifications are based on published cross-population pharmacokinetic comparisons for nifedipine (Castañeda-Hernández et al., 1992), sildenafil (Flores-Murrieta et al., 2000), and vonoprazan (Sakurai et al., 2015; Kagami et al., 2016; Scarpignato et al., 2022). Exposure–response and therapeutic window interpretations derive from the referenced literature and are discussed in detail in the main text. Classifications are conceptual and intended to illustrate the proposed framework rather than formal regulatory determinations. CASE-BASED APPLICATIONS 6.1 Nifedipine: presystemic metabolism and exposure shifts that are not automatically “clinically meaningful” Nifedipine illustrates how oral bioavailability and first-pass effects can produce apparent interpopulation differences in AUC and Cmax (Figure 1). In a comparative analysis including Mexican volunteers and data from other populations, Castañeda-Hernández and colleagues reported higher exposure metrics in Mexicans than in several non-Mexican cohorts (Castañeda-Hernández et al., 1992). Mechanistically, this pattern is consistent with differences in presystemic metabolism and possibly formulation or study-condition differences rather than fundamental changes in elimination. From a therapeutic window perspective, nifedipine is generally regarded as having a comparatively wide functional window within usual dosing, particularly when clinicians titrate based on blood pressure response and tolerability. This does not mean exposure differences are irrelevant; rather, it means that the system has built-in buffers: dose titration, clinical monitoring, and the absence of abrupt “all-or-none” toxicity within typical exposure ranges. Consequently, the existence of higher mean AUC in Mexicans does not by itself imply that foreign efficacy evidence is not applicable. The appropriate regulatory response is usually dose-awareness and labeling consideration, especially in subgroups with hypotension risk or interacting comedications, rather than duplicating efficacy trials. In the decision matrix, nifedipine therefore occupies a “low-risk extrapolation” quadrant: observed PK variability may be moderate to high, but exposure–response is relatively shallow, the functional window is wide, and residual uncertainty is low. A targeted PK evaluation could be justified if a sponsor plans a fixed-dose strategy without titration, or if co-medication patterns suggest a high probability of enzyme inhibition that could push exposure higher. Otherwise, foreign clinical data are typically acceptable with region-appropriate prescribing guidance. Figure 1. Cross-population comparison of mean nifedipine exposure (AUC) with empirical exposure reference band. Mean AUC values are derived from published pharmacokinetic studies in different populations, including Mexican volunteers (Castañeda-Hernández et al., 1992). The shaded region represents an empirical exposure reference band based on non-Mexican mean values and is intended for visualization of cross-population variability rather than definition of a formal therapeutic window. Error bars indicate variability as reported in the original studies. 6.2 Sildenafil: high PK variability with plateaued PD and limited regulatory consequence at standard doses Sildenafil provides a complementary example where exposure differences can be quantitatively larger yet still have limited regulatory consequence because of PD characteristics. Flores-Murrieta and colleagues evaluated sildenafil pharmacokinetics in Mexican men and contextualized their results against prior reports in other populations, including White cohorts (Flores-Murrieta et al., 2000). The analysis suggests that mean exposure (AUC) in Mexicans can be higher, approximately on the order of a two-fold difference, while Tmax remains similar, consistent with altered clearance and/or first-pass metabolism rather than radically different absorption kinetics (Figure 2). The clinical interpretation depends on the exposure–response curve. For erectile dysfunction, sildenafil efficacy tends to reach a plateau within commonly used dose ranges for many patients, while tolerability concerns (e.g., headache, flushing, hypotension) increase with dose and exposure but are generally manageable within labeled dosing. In this setting, a moderate exposure increase at a fixed dose may not proportionally increase efficacy, and safety signals are unlikely to become abruptly severe in most patients absent contraindicated co-medications (e.g., nitrates). Therefore, a higher AUC in one population does not necessarily justify local efficacy or safety duplication, provided labeling and contraindications are respected and interaction risks are addressed through standard clinical safeguards. Regulatory residual uncertainty for sildenafil is often dominated by drug–drug interactions and contraindicated combinations rather than by average population PK differences. This aligns with an extrapolation stance in which foreign efficacy evidence is acceptable and local confirmatory trials are not justified solely by exposure differences. If additional evidence is needed, it is more efficient to focus on interaction risk management and prescribing behaviors in the target region (a pharmacoepidemiologic question) rather than repeating efficacy trials. Figure 2. Cross-population comparison of mean sildenafil exposure (AUC) with empirical exposure reference band. Mean AUC values are based on published pharmacokinetic studies comparing Mexican and non-Mexican populations (Flores-Murrieta et al., 2000). The shaded region represents an empirical exposure reference band derived from comparator population means and is shown for visualization of cross-population variability. It does not represent a formal therapeutic window. Error bars reflect variability as reported in the original studies. 6.3 Vonoprazan: broad extrapolation with targeted uncertainty (DDIs, long-term safety, local implementation) Vonoprazan illustrates how mechanistic PK/PD features can support broad extrapolation, while also highlighting the boundaries of a purely PK-based argument. Proton pump inhibitors (PPIs) have well-recognized limitations: acid-dependent activation, delayed maximal effect, short plasma half-life despite longer pharmacodynamic effect, and clinically important CYP2C19 genotype dependence that contributes to variable acid suppression (Shin & Kim, 2013; El Rouby et al., 2018; Katz et al., 2022). In contrast, potassium-competitive acid blockers (PCABs) such as vonoprazan inhibit the H+/K+-ATPase by competing at the potassium-binding site, producing rapid and sustained acid suppression and reducing dependence on acid activation kinetics (Sakurai et al., 2015). Vonoprazan is absorbed relatively quickly, and early clinical pharmacology demonstrated robust 24-hour intragastric pH control from early dosing, including nocturnal suppression (Sakurai et al., 2015). From a PK standpoint, vonoprazan metabolism is driven primarily by CYP3A4 with smaller contributions from other pathways, decreasing CYP2C19 as a dominant driver of exposure variability compared with many PPIs (Echizen, 2016; Kagami et al., 2016). This supports more consistent performance across CYP2C19 genotype strata and across regions where CYP2C19 distributions differ. Clinical trials have reported efficacy at least non-inferior to lansoprazole for erosive esophagitis, with particular value in more severe disease in some analyses (Ashida et al., 2016). Trials in Western contexts support effectiveness in Helicobacter pylori eradication regimens, aligning with global acceptability goals (Chey et al., 2022). Population PK analyses and reviews have suggested that demographic covariate effects on exposure are modest, reinforcing the plausibility of extrapolation across regions when dosing is within tested ranges and PD endpoints are robust (Scarpignato et al., 2022). However, vonoprazan also demonstrates why extrapolation should not be framed as “no local evidence ever needed.” The most clinically relevant residual uncertainties may not be average exposure differences but interaction scenarios and long-term safety under chronic use. For example, gastroprotection in patients receiving antiplatelet therapy raises questions about pharmacodynamic interactions through CYP pathways and platelet inhibition. Kagami and colleagues examined effects of vonoprazan and esomeprazole on antiplatelet function of clopidogrel or prasugrel in relation to CYP2C19, illustrating that even if the primary PD endpoint (acid suppression) is robust, clinically meaningful uncertainty can remain in common co-medication contexts (Kagami et al., 2018). Additionally, long-term maintenance therapy raises safety questions that may be best addressed through post-authorization surveillance and real-world studies; large programs such as VISION provide evolving evidence relevant to chronic exposure management (Uemura et al., 2025). In Mexico, where direct clinical PK studies for vonoprazan are unavailable, a rational regulatory strategy is therefore conditional extrapolation: accept foreign efficacy data supported by mechanistic plausibility and cross-region PK/PD consistency, while prioritizing targeted evidence generation for interaction risk, long-term safety, and local implementation factors such as resistance patterns and prescribing behaviors (Figure 3.). This approach aligns with ICH E5/E17 and with the pharmacoepidemiologic perspective of PDS, where the highest-value local evidence may come from real-world effectiveness and safety studies rather than redundant confirmatory trials. Figure 3. Cross-population pharmacokinetic exposure comparisons for nifedipine, sildenafil, and vonoprazan with empirical exposure reference bands. Mean AUC values are derived from published pharmacokinetic studies comparing different populations: nifedipine (Castañeda-Hernández et al., 1992), sildenafil (Flores-Murrieta et al., 2000), and vonoprazan (Sakurai et al., 2015; Kagami et al., 2016; Scarpignato et al., 2022). For visualization purposes, shaded regions represent empirical exposure reference bands based on comparator population mean values and are intended to illustrate cross-population variability rather than define formal therapeutic windows. Error bars reflect variability as reported in the original studies. Differences in study design, formulation, dose, and sampling conditions may contribute to observed variability across panels. DISCUSSION Although this manuscript is concept-driven, its core claims imply testable hypotheses that are directly relevant to pharmacoepidemiology and drug safety. First, when local duplication is avoided based on PK/PD and therapeutic window reasoning, regulators should still ensure that post-authorization systems can detect unexpected safety signals and characterize effectiveness in routine care. Second, “residual uncertainty” often lies in co-medication patterns, comorbidity distributions, and adherence, areas where pharmacoepidemiologic methods are most informative. Importantly, the empirical exposure reference band is not equated with a therapeutic window but serves as a visualization tool for cross-population exposure dispersion. For the three case drugs, the highest-yield local evidence questions differ. For nifedipine, the priority may be understanding real-world dose titration, hypotension events, and co-prescribing with CYP3A inhibitors in older adults. For sildenafil, the priority is contraindicated co-use (nitrates, alpha-blockers) and cardiovascular risk stratification, rather than efficacy per se. For vonoprazan, priorities include interaction outcomes in antiplatelet users, long-term safety patterns, and regimen effectiveness in the context of local H. pylori resistance and adherence. These questions can be addressed using linked healthcare databases, electronic health records, claims data, or prospective registries, provided transparency standards and reproducibility expectations are met. Aligning the PK/PD-based extrapolation argument with a clear plan for local pharmacoepidemiologic monitoring may also increase reviewer and regulator confidence that patient protection is preserved without unnecessary delays. CONCLUSION Regulatory extrapolation should be grounded in mechanistic PK/PD understanding and therapeutic window reasoning rather than geographic assumptions. ICH E5 and ICH E17 provide the structural basis for accepting foreign clinical data and prospectively designing globally acceptable evidence, but their implementation benefits from explicit pharmacological criteria that define when variability is clinically meaningful. The nifedipine and sildenafil cases demonstrate that PK differences may be substantial yet remain clinically buffered when exposure–response is shallow or plateaued, and the functional therapeutic window is wide. Vonoprazan further illustrates how potent, rapid, and sustained PD with reduced CYP2C19 dependence can support broad regional extrapolation, while interaction scenarios and long-term safety define the most relevant targets for local evidence generation. Overall, a therapeutic window–guided framework supports risk-based regulation: prioritize local studies only when mechanistic and clinical plausibility indicate meaningful shifts in efficacy or safety, minimizing access delays while maintaining patient protection. Acknowledgements A.I.G.-M. acknowledges financial support from SECIHTI (Secretaría de Ciencia, Humanidades, Tecnología e Innovación) through a doctoral scholarship (CVU 657663). References Handoo, S., Arora, V., Khera, D., Nandi, P. K., & Sahu, S. K. (2012). A comprehensive study on regulatory requirements for development and filing of generic drugs globally. International Journal of Pharmaceutical Investigation, 2 (3), 99–105. https://doi.org/10.4103/2230-973X.104392 Valverde, J. L. (2016). The globalization of medicines as a challenge for governments. Pharmaceuticals Policy and Law, 18 (1–4), 19–29. https://doi.org/10.3233/PPL-160429 Wilson, J. L., Cheung, K. W. K., Lin, L., Green, E. A. E., Porrás, A. 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Keywords ethnic sensitivity multiregional clinical trials pharmacodynamic response pharmacoepidemiology pharmacokinetic variability regulatory extrapolation therapeutic window Authors Affiliations Aldo Iván García-Moncayo Cinvestav View all articles by this author Gilberto Castañeda-Hernández [email protected] Cinvestav View all articles by this author Metrics & Citations Metrics Article Usage 178 views 114 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Aldo Iván García-Moncayo, Gilberto Castañeda-Hernández. Therapeutic Window–Guided Regulatory Extrapolation: A Pharmacokinetic and Pharmacodynamic Framework for Deciding When Local Studies Are Necessary. Authorea . 01 March 2026. DOI: https://doi.org/10.22541/au.177238089.98460793/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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