---
title: "Clinical Dose Optimization: From Phase 2 Dose Finding to FDA Project Optimus"
description: Explore how PK–PD biomarkers, exposure–response modeling and fit-for-purpose bioanalysis support clinical dose optimization and FDA Project Optimus
image: https://www.crystalbiosolutions.com/hubfs/AI-Generated%20Media/Images/Researcher%20Analyzing%20Colorful%20Drug%20Vials%20In%20Sophisticated%20Lab.png
---
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Oct 14, 2026
# Clinical Dose Optimization: From Phase 2 Dose Finding to FDA Project Optimus
Explore how PK–PD biomarkers, exposure–response modeling and fit-for-purpose bioanalysis support clinical dose optimization and FDA Project Optimus
\\
## Introduction: why clinical dose optimization matters
Selecting the right clinical dose is more complex than finding the highest dose a patient can tolerate. Drug developers need to understand how exposure relates to pharmacological activity, clinical efficacy and safety, and whether a higher dose adds meaningful benefit.
Outside oncology, randomized Phase 2 dose-finding studies have traditionally established the dose carried into confirmatory trials. In oncology, FDA's Project Optimus reinforces a more comprehensive approach: sponsors are encouraged to characterize dose–response relationships and optimize the balance between efficacy and tolerability before registration.
Making these decisions with confidence depends on integrating pharmacokinetics (PK), pharmacodynamics (PD), biomarker data, exposure–response (E–R) analyses and reliable bioanalytical methods.
This article draws on a presentation by Lorin Roskos, Ph.D., Chief Scientific Officer and Head of Quantitative Clinical Pharmacology (QCP) at Crystal Bio Solutions, at the [CBS Scientific Symposium](https://www.crystalbiosolutions.com/news-and-events/cbs-scientific-symposium-reception) on October 13, 2026. Using a Phase 2 dose-finding framework and three case studies from biologics development, it shows how QCP and fit-for-purpose bioanalysis inform dose selection, and why both should be planned together from the earliest stages of development.
## From randomized Phase 2 dose finding to Project Optimus
Dose is the thread that runs through every stage of QCP work: the first-in-human (FIH) dose justification in the nonclinical package, the Phase 2 dose recommendation, and the Phase 3 and labeled dose. Decisions made early, such as which biomarkers to measure, which assays to use and which dose levels to test, constrain what can be justified later.
## The traditional path to a single Phase 3 dose
Outside oncology, the conventional route narrows a tolerated dose range in Phase 1 to one confirmed dose in Phase 3, with a randomized Phase 2 study doing most of the dose-selection work.
| Phase | Typical design | What it contributes to dose |
| --- | --- | --- |
| Phase 1 | Single and multiple ascending dose (SAD/MAD) cohorts | Safety, tolerability and PK; exploratory PD biomarkers; defines the tolerated dose range |
| Phase 2 (proof of concept and dose finding) | Randomized parallel groups, often several doses vs placebo | Efficacy and PD biomarker endpoints; characterizes exposure–response |
| Phase 3 (confirmatory) | Selected dose vs placebo or standard of care | Confirms benefit–risk at the chosen dose in adequate, well-controlled trials |
### An illustrative Phase 2 dose-finding design
Designs vary by program, but a common approach randomizes patients 1:1:1:1 to placebo and three dose levels. Each arm has a defined role on the exposure–response curve:
- **Placebo** anchors the placebo response.
- **Low dose** is expected to be suboptimal and sits on the rising part of the curve. Without it, every tested dose may sit on the plateau, and the study cannot show where efficacy begins to fall off.
- **Mid dose** provides mid-range exposure.
- **High dose** sits near the plateau, with its upper bound set by PD or safety.
Endpoints typically include primary efficacy, a PD biomarker, sparse PK sampling for population PK, and safety and tolerability. The aim is for the biomarker strategy to bracket the dose range, so the study shows not only that a drug works but which dose is appropriate.
## Oncology: from maximum tolerated dose to Project Optimus
Oncology development has historically relied on the maximum tolerated dose (MTD), particularly for cytotoxic therapies. Phase 1 escalation continues until dose-limiting toxicities (DLTs) define the MTD, and that dose often moves into expansion cohorts across several indications, supporting a randomized Phase 3 trial or accelerated approval without a separate randomized Phase 2 study.
For many molecularly targeted therapies and biologics, however, the highest tolerated dose may not give the best benefit–risk profile. Efficacy can plateau while adverse events keep rising, which can lead to unnecessary toxicity, dose reductions, treatment interruptions or discontinuations.
FDA's Oncology Center of Excellence launched Project Optimus to reform this approach, and in August 2024 FDA finalized its guidance, [Optimizing the Dosage of Human Prescription Drugs and Biological Products for the Treatment of Oncologic Diseases.](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/optimizing-dosage-human-prescription-drugs-and-biological-products-treatment-oncologic-diseases) The guidance covers dosage optimization during clinical development before approval; it does not address selection of the first-in-human starting dose. Its goal is to integrate efficacy, safety, tolerability, PK, PD and other relevant evidence into dose selection earlier in development.
The guidance does not prescribe one trial design. Depending on the program, sponsors may use randomized dose-comparison studies, exposure–response analyses, dose–response modeling and other complementary approaches. One practical option within an expansion strategy is to randomize patients 1:1 between a lower and a higher dose in one or two indications while other cohorts continue at the higher dose. The question is no longer only how much drug a patient can tolerate, but which dose gives the most appropriate balance of benefit and risk.
## How PK, PD biomarkers and exposure–response guide dose selection
Biomarkers and exposure–response analyses help define both ends of a credible dose range. They inform dose selection, but they do not replace clinical efficacy and safety endpoints, which remain necessary to establish benefit–risk.
| | Upper end of the dose range | Lower end of the dose range |
| --- | --- | --- |
| What biomarkers can show | A plateau in a relevant PD biomarker can indicate that more exposure may produce diminishing pharmacological effect | Reduced but meaningful PD activity can support a biologically active lower dose |
| Interpretation limits | A biomarker plateau does not by itself establish that clinical efficacy has plateaued | Reduced PD activity may mean reduced pharmacological effect; its impact on efficacy must be established in context |
| Oncology consideration | The highest dose may be capped by the MTD | The lower dose should have a credible chance of preserving efficacy |
| Supporting analysis | Exposure–safety analyses characterize the risks of higher exposure | Exposure–efficacy analyses characterize the benefits of higher exposure |
In non-oncology programs, the tested range can span from low PD activity on the rising part of the curve to maximum biomarker activity. In oncology the window is usually narrower, running from a dose with meaningful PD activity up to the MTD.
## Matching the biomarker to the question
Different biomarkers sit at different points between the drug regimen and the clinical outcome, and carry different weight in a dose decision:
- **Target engagement and pathway modulation** markers show that the drug reaches its target and has a pharmacological effect.
- **Disease modulation** markers may provide evidence closer to clinical benefit, depending on their validation and context.
- **Safety biomarkers** track on-target or off-target effects that may predict safety outcomes.
- **Predictive biomarkers** identify patients more likely to respond or to experience toxicity.
- **Prognostic biomarkers** relate to outcome regardless of treatment. They matter for dose selection because they can confound exposure–response analyses, as the tremelimumab case below shows.
For biologics, target-mediated drug disposition also links target biology to PK, and should be considered when interpreting both.
## Three case studies: lessons from biologics development
Three published examples show how assay choice and biomarker interpretation shape dose decisions in practice.
### 1. MEDI3726: choosing the right PK assay for an antibody–drug conjugate
**Challenge.** MEDI3726 is an anti-PSMA antibody–drug conjugate (ADC) carrying a pyrrolobenzodiazepine (PBD) warhead through a cleavable valine–alanine linker. In Phase 1, the active ADC was cleared faster than expected, and the cause was not obvious from a single PK measurement.
**Analysis.** A multi-assay approach including LC-MS/MS measured several species in parallel: active ADC, total ADC, total conjugated ADC, intact IgG and total IgG. The rapid loss of active ADC was associated with catabolism of the antibody scaffold rather than deconjugation of the PBD warhead. In the data Dr. Roskos presented at the symposium, AUC of active ADC was about 207 µg·h/mL versus 495 µg·h/mL for total ADC, and intact IgG was about 196 versus 311 µg·h/mL for total IgG (adapted from Huang et al., 2020).
**Lesson for dose selection.** A single total-antibody assay would have hidden the loss of active drug. Exposure–response models built on the wrong analyte can point to the wrong dose and the wrong engineering fix. (For more on ADC analyte strategy, see [ADC Bioanalysis: DAR Profiling and Stability Analysis](https://www.crystalbiosolutions.com/news-and-events/adc-bioanalysis-dar-profiling-and-stability-analysis).)
### 2. Anifrolumab (SAPHNELO®): translational dose selection with a PD biomarker
**Challenge.** Anifrolumab blocks the type I interferon (IFN) receptor. The goal was to move quickly from a Phase 1 study in systemic sclerosis (SSc) to a dose-ranging study in systemic lupus erythematosus (SLE), before any SLE efficacy data existed.
**Analysis.** A type I IFN gene signature (IFNGS) is elevated in the blood and skin of many patients with SLE and SSc. Wang et al. (2013) built a PK–PD model from Phase 1 SSc data that accounted for receptor internalization and the different signature levels in the two diseases. Simulations predicted that monthly IV doses of 300 mg or 1,000 mg would suppress the blood signature to healthy levels in about 53% and 68% of SLE patients, respectively. Both doses were taken into a randomized Phase 2b study, and subsequent clinical data established the approved SLE regimen of 300 mg every four weeks.
**Lesson for dose selection.** A well-characterized PD biomarker let modeling in one population inform dose selection in another. The biomarker model guided which doses to test; clinical efficacy and safety data determined the final dose.
### 3. Tremelimumab: when exposure–response is confounded
**Challenge.** In a randomized, placebo-controlled Phase IIb study of tremelimumab in unresectable malignant mesothelioma, there was no clinically meaningful difference in overall survival (OS). Yet patients with higher drug exposure appeared to live longer, raising the question of whether a higher dose would have worked.
**Analysis.** Population PK modeling identified sex, C-reactive protein (CRP) and baseline tumor size as predictors of clearance. CRP and tumor burden were also associated with OS regardless of treatment, and prognostic factors were imbalanced in favor of patients in the highest exposure quartile. Patients with high CRP made up most of the lowest exposure quartile and a minority of the highest. The authors concluded that the exposure–OS relationship was likely spurious rather than a true exposure-driven effect (Baverel et al., 2019). Related work on durvalumab has linked antibody clearance to longitudinal markers of disease status (Baverel et al., 2018).
**Lesson for dose selection.** An apparent exposure–response signal can reflect disease severity, which affects both drug clearance and survival, rather than a drug effect. Observational exposure–response analyses need prognostic biomarkers and multivariate methods before they are used to justify a higher dose.
## Integrating quantitative clinical pharmacology and bioanalysis for better dose decisions
All three case studies make the same point: a dose recommendation is only as reliable as the PK, PD and biomarker data behind it. In Dr. Roskos's words, bioanalysis puts the "P" in QCP. (For a deeper look at how assay format and analyte choice shape population PK/PD models, see [Population PK/PD Modeling That Starts With Better Bioanalysis](https://www.crystalbiosolutions.com/news-and-events/population-pk/pd-modeling-that-starts-with-better-bioanalysis).)
Clinical dose optimization therefore needs a coordinated strategy from the start of a program, not an analysis added once a dose question arises. At Crystal Bio Solutions, the QCP and bioanalytical and biomarker teams work together on:
- Translational and clinical PK–PD modeling, from FIH dose justification onward
- Population PK and exposure–response analysis for Phase 2 and Phase 3 dose recommendations
- Exposure–safety assessment to characterize the risks of higher exposure
- Bioanalytical method development and validation, including multi-analyte methods for complex modalities such as ADCs
- Biomarker assay strategy and interpretation to bracket the dose range
- Support for health authority interactions, including End-of-Phase 2 meetings
**Planning a dose-finding study or evaluating alternative dosing regimens?** Talk with the CBS teams about your PK–PD modeling, exposure–response, bioanalytical and biomarker needs. Email cbs\_bd@crystalpharmatech.com or call +1 609-604-8303.
## Frequently asked questions
### What is FDA Project Optimus?
Project Optimus is an FDA Oncology Center of Excellence initiative to move oncology dose selection beyond the maximum tolerated dose. FDA's final guidance, issued in August 2024, asks sponsors to characterize dose–response and select a dosage that optimizes efficacy, safety and tolerability. Randomized dose comparisons are one tool, not a universal requirement.
### How is an optimized dose different from the maximum tolerated dose?
The MTD is the highest dose with acceptable dose-limiting toxicity. An optimized dose is chosen for the best balance of benefit and risk, which for many targeted therapies and biologics may be lower than the MTD if efficacy plateaus while toxicity keeps rising.
### Does a plateau in a PD biomarker mean efficacy has plateaued?
Not necessarily. A biomarker plateau suggests that more exposure may add little pharmacological effect, which can support dose selection. Whether clinical efficacy has also plateaued must be confirmed with exposure–response analyses, clinical endpoints and safety data.
### Can exposure–response data be misleading?
Yes. Baseline disease severity can increase drug clearance and worsen outcomes at the same time, producing an apparent exposure–response relationship that is not causal. Prognostic biomarkers such as CRP and multivariate analysis help separate drug effect from confounding.
### When should dose-optimization planning start?
Dose-optimization planning should begin early in clinical development. Decisions about PK sampling, biomarker selection, assay performance and the dose levels tested in early studies determine the evidence available for later dose recommendations. FDA's oncology dosage guidance focuses on optimizing dosage during clinical development before approval; it does not address selection of the initial first-in-human dose.
## References
1. Huang Y, et al. Multifaceted Bioanalytical Methods for the Comprehensive Pharmacokinetic and Catabolic Assessment of MEDI3726, an Anti-Prostate-Specific Membrane Antigen Pyrrolobenzodiazepine Antibody–Drug Conjugate. Anal Chem. 2020;92(16):11135–11144. [doi:10.1021/acs.analchem.0c01187](https://doi.org/10.1021/acs.analchem.0c01187)
2. Wang B, Higgs BW, Chang L, et al. Pharmacogenomics and Translational Simulations to Bridge Indications for an Anti-Interferon-α Receptor Antibody. Clin Pharmacol Ther. 2013;93(6):483–492. [doi:10.1038/clpt.2013.35](https://doi.org/10.1038/clpt.2013.35)
3. Baverel P, Roskos L, Tatipalli M, et al. Exposure–Response Analysis of Overall Survival for Tremelimumab in Unresectable Malignant Mesothelioma: The Confounding Effect of Disease Status. Clin Transl Sci. 2019;12(5):450–458. [doi:10.1111/cts.12633](https://doi.org/10.1111/cts.12633)
4. Baverel PG, Dubois VFS, Jin CY, et al. Population Pharmacokinetics of Durvalumab in Cancer Patients and Association With Longitudinal Biomarkers of Disease Status. Clin Pharmacol Ther. 2018;103(4):631–642. [PMC5887840](https://pmc.ncbi.nlm.nih.gov/articles/PMC5887840)
5. U.S. Food and Drug Administration. [Optimizing the Dosage of Human Prescription Drugs and Biological Products for the Treatment of Oncologic Diseases: Guidance for Industry](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/optimizing-dosage-human-prescription-drugs-and-biological-products-treatment-oncologic-diseases). August 2024.
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