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Pharmacology fundamentals
Peptide Pharmacodynamics, Potency, Efficacy & Dose–Response
How target binding, exposure and biological response are measured and interpreted — and why a laboratory potency is not a ranking of compounds or a treatment dose.
Affinity, potency and efficacy answer three different questions: how tightly a compound binds, how much of it is needed to produce a chosen response, and how large a response it can produce. Treating them as one number — or reading a laboratory result as a treatment dose — is the most common way pharmacodynamic data get misread. This guide separates them, shows how each is read from a concentration–response curve, and marks where laboratory results stop translating to people.
What pharmacodynamics means
Pharmacodynamics (PD) describes what a compound does to its target and to the body, and how that response changes with exposure. Pharmacokinetics (PK) describes how the body handles the compound over time: how much reaches the site of action and for how long.
The two are linked, not independent. The response seen at any moment depends on how much compound is present at the target, and that depends on PK. Regulatory guidance studies that chain as exposure–response relationships, and individual people can differ in response even at the same blood concentration.
Response can be measured at different levels: binding to a target, a signalling readout in cells, a biomarker in blood or tissue, an imaging measure, or a clinical endpoint — how a patient feels, functions or survives. Each level answers a different question, and a result at one level does not automatically carry to the next.
Figure 1 · Conceptual illustration — not data
Pharmacokinetics and pharmacodynamics are linked
Step 1
Exposure
Compound present at the target (pharmacokinetics).
Step 2
Target
Binding and occupancy.
Step 3
Signal
Intracellular signalling.
Step 4
Response
Measured biological effect.
Lag and feedback: the response can trail exposure in time, so the two are read over time. Steps 1–4 are one chain, not two separate topics.
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From target binding to biological response
In the simplest picture, a compound meets its target and binds (affinity). The share of targets with compound bound is the occupancy. Binding can trigger signalling inside the cell, and signalling produces the biological response that is finally measured.
Each step can be measured separately, and each can behave differently in different systems. Measured potency and maximal response, for example, depend on the assay system, including how much of the receptor the cells carry.
This guide follows that chain in order. Section 14 extends it beyond the dish, from laboratory concentration to exposure, target engagement, response and clinical endpoint.
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Affinity
Affinity describes how tightly a compound binds its target, and so how much of the target is occupied at a given concentration. It is not efficacy and it is not potency.
The equilibrium dissociation constant, Kd, is often described as the concentration at which half the receptors are occupied. That description holds only under a simple model: reversible binding, at equilibrium, to a single kind of site, one compound to one site. A measured Kd also depends on how it was measured, so it is not a universal constant of the compound.
Binding strength and the size of the response are separate properties. In one published in-vitro study of the dual GIP and GLP-1 receptor agonist tirzepatide, the authors measured affinity, cAMP potency and maximal signalling at the GLP-1 receptor as separate quantities. Several of those authors are affiliated with the company that developed the compound, and the work was done in cell systems, not in people.
Competition-binding assays report two further quantities that are often confused. An IC50 is the concentration of a competing compound that reduces binding or a response by 50% under that assay's conditions, so it depends on those conditions and is not itself an affinity. A Ki, the inhibitor's equilibrium dissociation constant, is calculated from an IC50 using the assay's labelled-ligand concentration and Kd, under stated conditions such as a Hill slope near one. The two are related, but they are different quantities.
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Potency
Potency describes how much of a compound is needed to produce a chosen level of response in a particular experimental system. Researchers usually summarize it with the EC50, the concentration that produces half of that compound's own maximum response. In the same assay, a compound with a lower EC50 is more potent. That does not make it a better medicine. Potency says nothing about how large a response the compound can produce, how safe it is, or how it performs in people.
An EC50 is a concentration measured in an assay. It is not a dose, and it does not tell anyone how much of a compound to use.
Two versions of the number exist. A relative EC50 is the midpoint between the fitted curve's own bottom and top, while an absolute EC50 is the concentration giving 50% of a range defined by controls. They can give different numbers for the same data, so an assay should use one definition consistently.
Potency also depends on the system. For the same compound, measured potency changes with the amount of receptor the cells carry, so a lower EC50 only means more potent when the systems being compared are comparable.
Efficacy and Emax
In pharmacology, efficacy describes how large a response a compound can produce in a given system once it is bound to its target. It is often reported as Emax, the largest response observed, compared with a reference compound in the same assay. This is different from clinical efficacy, which asks whether a treatment benefits patients in a clinical trial. A large Emax in a cell assay is a laboratory measurement. It is not evidence that a therapy works.
Relative efficacy compares a compound's fitted maximum with the maximum of a reference compound in the same assay. Because it depends on the reference chosen, a relative efficacy is a statement about one assay, not a property carried by the compound everywhere.
Potency versus efficacy
Potency is where along the concentration axis a curve rises. Efficacy is how high it reaches. The two can differ independently: greater potency does not mean a larger maximal response, and a larger maximal response does not imply greater potency.
Figure 2 · Conceptual illustration — not data
Potency: the same maximum, different positions
Figure 3 · Conceptual illustration — not data
Efficacy: similar EC50, different plateaus
A larger plateau in a laboratory system is not a clinical result.
In Figure 2, Compound A is more potent than Compound B in this illustrative system. That is a statement about where each curve sits, not a judgement that one is better. In Figure 3, the two curves rise in a similar region but reach different plateaus. A larger plateau in a laboratory system is not a clinical result.
Reading a concentration–response curve
Concentration–response data are commonly summarised with a sigmoid, Hill-type curve. Concentration is usually plotted on a logarithmic axis, so the curve is S-shaped: little response at low concentration, a rising region, and a plateau where more compound adds little. The EC50 marks the midpoint of the rise and the plateau gives Emax.
Fitted values are only as good as the fit. EC50 and IC50 values are trustworthy only when the fitted curve adequately defines its plateaus; a curve that does not level off may not give a reliable midpoint or maximum.
Figure 4 · Conceptual illustration — not data
A concentration–response curve
Go deeper: the Hill slope
The Hill slope (or Hill coefficient) describes how steep the curve is. It is a fitted descriptor of steepness. By itself it does not reveal the number of binding sites and does not prove cooperativity.
Full agonists, partial agonists and antagonists
An agonist activates its receptor. Whether an agonist counts as full or partial is judged against another agonist at the same receptor in the same system, not as an absolute property of the compound.
A partial agonist activates its target but cannot produce as large a response as a full agonist in the same system, even when every receptor is occupied. That is not the same as being weak or half-strength. What a compound can produce depends on the system, so the same compound can look like a full agonist in one cell type and a partial agonist in another.
A published experimental example shows how much the system matters. In cell studies of (Pro3)GIP, the peptide behaved close to a full agonist at the human GIP receptor, but as a partial agonist and a competitive antagonist at rat and mouse GIP receptors. This is one ligand in one set of experimental systems; it does not mean every partial agonist behaves this way.
An antagonist blocks the effect of an agonist. In the (Pro3)GIP study, the competitive behaviour appeared as a shift of the agonist's concentration–response curve to higher concentrations. Panel C of Figure 5 sketches that idea, which can occur in some systems.
Figure 5 · Conceptual illustration — not data
Full and partial agonists depend on the system
A. Same system
B. Higher receptor expression
C. With a full agonist (can occur in some systems)
Receptor occupancy and signal amplification
Occupancy is the share of receptors that have a compound bound. Response does not always follow it one to one. Cells can amplify a signal, so a strong response can appear when only a fraction of the receptors are occupied. For that reason, 50% occupancy does not necessarily mean 50% of the maximum effect.
This is called signal amplification (traditionally called receptor reserve or spare receptors). The IUPHAR definition, as quoted by Buchwald (2025), describes spare receptors as being assumed when a full agonist can cause a maximum response while occupying only a fraction of the total receptor population. The idea of a receptor reserve was proposed in the mid-1950s.
The terminology is not settled. One author, Buchwald (2025), argues that the traditional label "spare receptors" is misleading and prefers "signal amplification". That is one author's position, not a consensus, and this page uses both names for that reason.
Amplification varies by pathway and system, and some systems show little or none. A response curve that sits to the left of the occupancy curve describes an amplifying system, not every system.
Figure 6 · Conceptual illustration — not data
Occupancy and response need not match
Amplifying system
Little or no amplification
View sources
Time: why effect can lag or persist
The effect of a compound does not always rise and fall with its concentration in the blood. Effect can lag behind, or outlast, plasma concentration.
One common reason is that many responses are indirect: the compound changes the production or removal of something else, which then changes the measured response. Indirect-response mechanisms give a slow onset and a slow return to baseline.
Cells can also become less responsive after stimulation. G protein-coupled receptors can desensitise through phosphorylation, arrestin binding and internalisation. This is a mechanism-level description; it is not a statement about any named peptide.
One narrow human example: in a study in healthy volunteers, the slowing of gastric emptying produced by a continuous intravenous infusion of GLP-1 was markedly diminished after about five hours. The authors interpreted this as tachyphylaxis. They did not draw firm conclusions for effects on islet hormones, and they noted that the finding does not directly settle therapeutic use. It does not establish a general rule of tolerance, and it does not describe how any current peptide product behaves.
Biased agonism and multi-receptor peptides
Biased agonism means that a ligand activates some signalling pathways more than others at the same receptor. Signalling bias can be measured, but predicting therapeutic value in the body from bias measured in vitro is difficult.
In vitro work on the dual agonist tirzepatide described biased signalling at the GLP-1 receptor, and the authors, several of whom are affiliated with the developing company, discussed it as potentially beneficial. A later study of GLP-1 receptor mutations noted that differences in affinity, efficacy, stability and pharmacokinetics confound interpretation of bias. The contribution of bias to clinical outcomes is not resolved by these studies.
Multi-receptor peptides act on more than one receptor, for example GIP and GLP-1, or glucagon as well. Taken together, the sources describe how "balanced" such a peptide is as depending on the system and the readout, not on one EC50 ratio. This page makes no comparison between named products and does not rank multi-receptor peptides.
Biologics that work differently
Not every drug acts as a receptor agonist. Antibodies can act by neutralising a ligand. Garetosmab, an antibody studied in fibrodysplasia ossificans progressiva (FOP), binds activin A and blocks its activation of the FOP-mutant ACVR1 receptor. Mouse studies reported that activin A drives the abnormal bone formation in FOP and that an anti-activin A antibody blocked it.
For antibodies that bind a membrane target, binding can itself be a route of elimination, called target-mediated drug disposition. It produces dose-dependent, nonlinear behaviour.
The garetosmab phase 2 trial is used here only to show how endpoint choice shapes what is concluded. In its first treatment period, the primary endpoints were imaging endpoints: the time-weighted change in total lesion activity measured by 18F-NaF PET, and the CT volume of heterotopic-ossification lesions. The PET total lesion activity measure did not differ significantly between groups (P = 0.0741). Post hoc analyses reported lower SUVmax in pre-existing lesions. For the second period, the hypothesis and endpoints were redefined by a protocol amendment after the first period. Different analyses of imaging readouts can tell different stories.
The lesson is about endpoint specificity and about the difference between pre-specified and post hoc analyses. It is not a verdict on the antibody: the trial report also describes safety findings that this page does not summarise, and no dose is given here.
PD biomarkers, target engagement, surrogate and clinical endpoints
These terms are often blurred. The FDA–NIH BEST glossary separates them, and the distinctions matter when a study reports a changed measurement.
- PD biomarker
- A measurable sign of biologic activity, without necessarily drawing conclusions about efficacy or disease outcome.
- Surrogate endpoint
- A biomarker-type endpoint used as a substitute for how a patient feels, functions or survives. It counts as a surrogate for efficacy only if it has been empirically shown to predict clinical benefit under the relevant regulatory framework.
- Clinical endpoint
- A measure of how a patient feels, functions or survives. (This is MitoCore's plain wording, built from the BEST definition of a surrogate endpoint; BEST defines "endpoint" only generically.)
- Target engagement
- Evidence that a compound is interacting with its intended target in a living system. Taken together, the sources suggest this helps interpret a lack of effect, but engagement alone does not demonstrate clinical benefit.
FDA exposure–response guidance notes that biomarker changes often follow a different time course from clinical endpoints, and that exposure–response analysis of biomarkers can help set dose ranges for clinical studies.
One related concept: the therapeutic index is typically considered the ratio of the highest exposure without toxicity to the exposure that produces the desired efficacy. It is a concept used in drug development, not a dosing rule.
Why laboratory potency is not a human dose
A concentration measured in a dish is not a dose. In the body, the amount of a compound that reaches its target depends on how it is absorbed, distributed and cleared, and the response also depends on the biology of the target tissue. Regulators therefore look at the whole chain, from dose to exposure to response, and at both desired and unwanted effects. Cell assays can guide that work, but they cannot turn a laboratory potency into a human dose.
Taken together, the sources describe dose selection as drawing on exposure and response data, including undesired effects, rather than on a laboratory potency alone. ICH E4 also describes the general shape of the problem: beyond some point on the dose–response curve for the desired effect, higher doses add little benefit, and any dose gives a mixture of desirable and undesirable effects.
No conversion from a laboratory concentration to a human dose is offered anywhere on this page, and none is implied. Figure 7 shows why: each step in the chain adds uncertainty.
Figure 7 · Conceptual illustration — not data
From a laboratory concentration to a clinical endpoint
Step 1
Laboratory concentration
What was applied to cells or proteins in an assay.
Step 2
Exposure
What reaches the body's tissues over time, shaped by pharmacokinetics.
Step 3
Target engagement
Evidence of interaction with the intended target in a living system.
Step 4
Biological response
A measurable effect, such as a biomarker change.
Step 5
Clinical endpoint
How a patient feels, functions or survives.
Not drawn: an arrow from a laboratory concentration (such as an EC50) straight to a human dose. No such conversion exists.
View sources
Why cross-study and cross-species comparisons mislead
Potency values from different studies should be treated as interchangeable only if the methods are sufficiently comparable. Taken together, the sources identify six things that can change an apparent potency: the receptor system and its expression level, the species, the readout, the reference agonist or normalisation, the incubation conditions, and how the curve was defined.
Two of these are worth a closer look. Normalising results to a low-efficacy or a high-efficacy reference agonist can make the same compounds look differently efficacious. And species can matter enough to reverse a ligand's behaviour: the (Pro3)GIP example in Section 8 acted close to a full agonist at the human receptor and as a partial agonist and competitive antagonist at rodent receptors.
For the same reasons, an animal dose–response relationship cannot be assumed to carry over to people, and this page offers no way to convert one to the other.
Figure 8 · Conceptual illustration — not data
Why the same compound can look different across studies
Receptor expression
Reference-agonist normalisation
Species
Common interpretation mistakes
Ten readings that often go wrong, each with what the cited evidence supports instead.
Interpretation 1
Common assumption: A lower EC50 means a better drug.
What the evidence supports: Within the same system, a lower EC50 means a more potent compound. That says nothing about maximal effect, safety or clinical value.
Caveat: This does not mean highly potent compounds are worse; it means potency alone is not a quality ranking. Sources: Buchwald 2025, Sparre-Ulrich 2016, Assay Guidance Manual
Interpretation 2
Common assumption: Higher affinity means higher efficacy.
What the evidence supports: Binding strength and the size of the response are separate properties.
Caveat: Tirzepatide data are an in-vitro illustration from a developer-affiliated study, not a rule. Sources: Buchwald 2025, Willard 2020
Interpretation 3
Common assumption: In-vitro potency gives the human dose.
What the evidence supports: It does not. Dose selection uses exposure and response data, including undesired effects.
Caveat: This is a summary across the cited sources, and no conversion is offered here. Sources: FDA 2003, ICH E4, Willard 2020
Interpretation 4
Common assumption: 50% occupancy means 50% of the effect.
What the evidence supports: Not necessarily. Systems can amplify a signal, so response can run ahead of occupancy.
Caveat: Amplification varies by pathway and system; not every system shows it. Sources: Buchwald 2025
Interpretation 5
Common assumption: A maximum response needs every receptor occupied.
What the evidence supports: In systems with amplification, a maximal response can occur with only part of the receptors occupied.
Caveat: The definition used is the IUPHAR wording as quoted in a 2025 review. Sources: Buchwald 2025
Interpretation 6
Common assumption: A larger Emax means more clinical benefit.
What the evidence supports: Emax is a measurement in one system. A laboratory or biomarker readout stands in for clinical benefit only if it has been validated as a surrogate.
Caveat: A larger plateau in a laboratory system is not a clinical result. Sources: FDA 2003, BEST glossary
Interpretation 7
Common assumption: Potencies from different assays are directly comparable.
What the evidence supports: Only when the methods are sufficiently comparable: system, species, readout, normalisation, incubation and curve definition all matter.
Caveat: This is a summary across the cited sources; each factor is sourced individually. Sources: Sebaugh 2011, Willard 2020, Sparre-Ulrich 2016, Assay Guidance Manual
Interpretation 8
Common assumption: Animal dose–response carries over to humans.
What the evidence supports: Pharmacology can differ by species, even reversing a ligand's behaviour.
Caveat: The example is one ligand and one receptor; no dose conversion is offered. Sources: Sparre-Ulrich 2016
Interpretation 9
Common assumption: Statistical significance means a result is important.
What the evidence supports: Statistical significance does not measure the size of an effect or the importance of a result, and it is not the same as scientific, human or economic significance.
Caveat: This does not make p-values meaningless; it limits what they can tell you. Sources: ASA 2016
Interpretation 10
Common assumption: More receptors activated means better efficacy.
What the evidence supports: How multi-receptor activity is balanced depends on the system and the readout, and the sources do not establish that more activity means better outcomes.
Caveat: In the cited in-vitro work, the authors describe the best balance as unclear. Sources: Buchwald 2025, Willard 2020, Tran 2026, Coskun 2022
Evidence limitations
What the evidence on this page can and cannot support.
L1Pharmacodynamic parameters are system-dependent: potency and maximal response change with receptor expression, readout and assay conditions. Assay Guidance Manual, Buchwald 2025, Willard 2020
L2In-vitro results may not predict human outcomes, and translating in-vitro signalling bias into in-vivo value is difficult. Kenakin 2024, Tran 2026
L3Cross-study potency comparisons need comparable methods. Assay Guidance Manual, Sebaugh 2011
L4Affinity is not efficacy. Buchwald 2025
L5A biomarker or imaging change is not automatically clinical benefit. FDA 2003, BEST glossary
L6Animal pharmacology may differ from human pharmacology. Sparre-Ulrich 2016
L7Desired and undesired effects can follow different dose and time relationships. ICH E4, FDA 2003
L8Clinical response depends on both pharmacokinetics and pharmacodynamics. FDA 2003, ICH E4
L9Published parameter estimates carry methodological uncertainty, for example from curve fitting and poorly defined plateaus. Sebaugh 2011, Assay Guidance Manual
L10Several key datasets here are in vitro, and some come from the developing company's own scientists, as noted where the tirzepatide study is cited. Where an affiliation has been verified this page says so. Willard 2020, Coskun 2022
L11The trial results shown are from single studies; endpoint choice and analysis timing change what is concluded. Di Rocco 2023
Key takeaways
- Affinity, potency and efficacy are different properties: how tightly a compound binds, how much is needed for a chosen response, and how large a response it can produce.
- A lower EC50 means more potent only within a comparable system, and it says nothing about maximal effect, safety or clinical value.
- Pharmacologic efficacy (Emax) is a laboratory measurement; clinical efficacy asks whether patients benefit in a clinical trial.
- A partial agonist is defined relative to a system and another agonist. It is not weak or half-strength, and it can behave as a full agonist in some systems.
- Occupancy and response need not match one to one, because signal amplification varies by pathway and system.
- A biomarker or imaging change is not automatically a clinical benefit, and target engagement alone does not demonstrate one.
- A concentration measured in a dish is not a human dose, and results from different assays, studies or species are comparable only when the methods are.
Source library
Every statement above relies on one or more of the sources below. Where a statement begins "Taken together", it is MitoCore's summary across the cited sources rather than a quotation from any one of them. Several datasets are in vitro or sponsor-affiliated, as noted in the text. Links point to PubMed, PubMed Central, regulator or NCBI Bookshelf records where they exist.
- RegulatoryU.S. Food and Drug Administration (CDER/CBER). Guidance for Industry: Exposure-Response Relationships — Study Design, Data Analysis, and Regulatory Applications. April 2003.FDA guidance (PDF)
- RegulatoryInternational Council for Harmonisation. ICH E4: Dose-Response Information to Support Drug Registration. Step 4, 10 March 1994 (CPMP/ICH/378/95).ICH guideline (PDF)EMA page
- Peer-reviewedSebaugh JL. Guidelines for accurate EC50/IC50 estimation. Pharmaceutical Statistics 2011;10(2):128–134. doi:10.1002/pst.426.PubMed 22328315
- Peer-reviewedBuchwald P. Quantification of signal amplification for receptors: the Kd/EC50 ratio of full agonists as a gain parameter. Frontiers in Pharmacology 2025;16:1541872. doi:10.3389/fphar.2025.1541872.PMC12011844PubMed 40264679
- Peer-reviewedSheiner LB, Stanski DR, Vozeh S, Miller RD, Ham J. Simultaneous modeling of pharmacokinetics and pharmacodynamics: application to d-tubocurarine. Clinical Pharmacology & Therapeutics 1979;25(3):358–371. doi:10.1002/cpt1979253358.PubMed 761446
- Peer-reviewedDayneka NL, Garg V, Jusko WJ. Comparison of four basic models of indirect pharmacodynamic responses. Journal of Pharmacokinetics and Biopharmaceutics 1993;21(4):457–478. doi:10.1007/BF01061691.PubMed 8133465
- Peer-reviewedDanhof M, de Jongh J, De Lange EC, Della Pasqua O, Ploeger BA, Voskuyl RA. Mechanism-based pharmacokinetic-pharmacodynamic modeling: biophase distribution, receptor theory, and dynamical systems analysis. Annual Review of Pharmacology and Toxicology 2007;47:357–400. doi:10.1146/annurev.pharmtox.47.120505.105154.PubMed 17067280
- Peer-reviewedWeiss JN. The Hill equation revisited: uses and misuses. The FASEB Journal 1997;11(11):835–841.PubMed 9285481
- Peer-reviewedGoutelle S, Maurin M, Rougier F, Barbaut X, Bourguignon L, Ducher M, Maire P. The Hill equation: a review of its capabilities in pharmacological modelling. Fundamental & Clinical Pharmacology 2008;22(6):633–648. doi:10.1111/j.1472-8206.2008.00633.x.PubMed 19049668
- Peer-reviewedFerguson SS. Evolving concepts in G protein-coupled receptor endocytosis: the role in receptor desensitization and signaling. Pharmacological Reviews 2001;53(1):1–24.PubMed 11171937
- Peer-reviewedKenakin T, Christopoulos A. Signalling bias in new drug discovery: detection, quantification and therapeutic impact. Nature Reviews Drug Discovery 2013;12(3):205–216. doi:10.1038/nrd3954.PubMed 23411724
- Peer-reviewedKenakin T. Bias translation: The final frontier? British Journal of Pharmacology 2024;181(9):1345–1360. doi:10.1111/bph.16335.PubMed 38424747
- Peer-reviewedWillard FS, Douros JD, Gabe MBN, Showalter AD, Wainscott DB, Suter TM, et al. Tirzepatide is an imbalanced and biased dual GIP and GLP-1 receptor agonist. JCI Insight 2020;5(17):e140532. doi:10.1172/jci.insight.140532. In vitro study; several authors are affiliated with the developing company.PMC7526454PubMed 32730231
- Peer-reviewedTran HD, Zuo Y, Wong C, Pollard A, Bloom S, Jones B. Modelling G protein-biased agonism using GLP-1 receptor C-terminal mutations. Molecular Metabolism 2026;105:102321. doi:10.1016/j.molmet.2026.102321.PMC12925471PubMed 41570980
- Peer-reviewedCoskun T, Urva S, Roell WC, Qu H, Loghin C, Moyers JS, et al. LY3437943, a novel triple glucagon, GIP, and GLP-1 receptor agonist for glycemic control and weight loss: from discovery to clinical proof of concept. Cell Metabolism 2022;34(9):1234–1247.e9. doi:10.1016/j.cmet.2022.07.013.PubMed 35985340
- Peer-reviewedSparre-Ulrich AH, Hansen LS, Svendsen B, Christensen M, Knop FK, Hartmann B, et al. Species-specific action of (Pro3)GIP — a full agonist at human GIP receptors, but a partial agonist and competitive antagonist at rat and mouse GIP receptors. British Journal of Pharmacology 2016;173(1):27–38. doi:10.1111/bph.13323.PMC4737396PubMed 26359804
- Peer-reviewedMuller PY, Milton MN. The determination and interpretation of the therapeutic index in drug development. Nature Reviews Drug Discovery 2012;11(10):751–761. doi:10.1038/nrd3801.PubMed 22935759
- RegulatoryFDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) Resource — Glossary. Silver Spring (MD): U.S. Food and Drug Administration; Bethesda (MD): National Institutes of Health. NCBI Bookshelf NBK338448.NCBI Bookshelf
- Society guidanceWasserstein RL, Lazar NA. The ASA statement on p-values: context, process, and purpose. The American Statistician 2016;70(2):129–133. doi:10.1080/00031305.2016.1154108.Publisher page
- Peer-reviewedMager DE, Jusko WJ. General pharmacokinetic model for drugs exhibiting target-mediated drug disposition. Journal of Pharmacokinetics and Pharmacodynamics 2001;28(6):507–532. doi:10.1023/a:1014414520282.PubMed 11999290
- Peer-reviewedDi Rocco M, Forleo-Neto E, Pignolo RJ, Keen R, Orcel P, Funck-Brentano T, et al. Garetosmab in fibrodysplasia ossificans progressiva: a randomized, double-blind, placebo-controlled phase 2 trial. Nature Medicine 2023;29(10):2615–2624. doi:10.1038/s41591-023-02561-8. Sponsor-conducted trial.PMC10579054PubMed 37770652
- Peer-reviewedHatsell SJ, Idone V, Wolken DM, Huang L, Kim HJ, Wang L, et al. ACVR1 R206H receptor mutation causes fibrodysplasia ossificans progressiva by imparting responsiveness to activin A. Science Translational Medicine 2015;7(303):303ra137. doi:10.1126/scitranslmed.aac4358.PMC6164166PubMed 26333933
- Reference manualAssay Guidance Manual [Internet]. Markossian S, Grossman A, Arkin M, et al., editors. Eli Lilly & Company and the National Center for Advancing Translational Sciences. Chapters cited: Glossary of Quantitative Biology Terms; Measurement of cAMP for Gαs- and Gαi-Protein-Coupled Receptors (GPCRs); Receptor Binding Assays for HTS and Drug Discovery; Assay Operations for SAR Support; Data Standardization for Results Management.GlossarycAMP measurementReceptor binding assaysAssay operations for SARData standardization
- Peer-reviewedNauck MA, Kemmeries G, Holst JJ, Meier JJ. Rapid tachyphylaxis of the glucagon-like peptide 1-induced deceleration of gastric emptying in humans. Diabetes 2011;60(5):1561–1565. doi:10.2337/db10-0474.PMC3292331PubMed 21430088
- Peer-reviewedSimon GM, Niphakis MJ, Cravatt BF. Determining target engagement in living systems. Nature Chemical Biology 2013;9(4):200–205. doi:10.1038/nchembio.1211.PMC4004587PubMed 23508173
- Peer-reviewedRyman JT, Meibohm B. Pharmacokinetics of monoclonal antibodies. CPT: Pharmacometrics & Systems Pharmacology 2017;6(9):576–588. doi:10.1002/psp4.12224.PMC5613179PubMed 28653357
Related MitoCore science
- Peptide Pharmacokinetics, Bioavailability & Half-Life →How exposure over time is shaped — the PK half of the chain.
- Mechanism & Pathway Explorer →Mechanism records and their evidence status.
- Peptide Stability →Why the material in an assay may not be the material that was weighed.
- Peptide Comparison →Side-by-side compound profiles. This is not a potency ranking.
- Research Timelines →How research on a compound developed over time.
- Peptide Immunogenicity →Another source of variability in exposure and response.
Related Learn topics
Continue in Research
Learn guides explain concepts and methods. Study records, compound evidence and mechanism records are in Research.