Compare FDA guidance to EMA & PMDA for in vitro DDI assessments

This online whitepaper article explores the 2020 regulatory changes in U.S. FDA guidance¹ and recommendations, compared to existing EU EMA² and Japanese PMDA³ guidance for in in vitro drug–drug interaction (DDI) assessments.

The Evolving Regulatory Landscape

Since 1997 the regulatory agencies, FDA, EMA and PMDA have endorsed the use of in vitro metabolism studies to assess the DDI potential of new chemical entities (NCE). Over the past 10 years, however, scientific progress has been rapid, fueled by sophisticated in vitro models and in silico predictive programs.

Today the use of modern in vitro techniques allows for more accurate prediction of the DDI potential of NCEs. With a greater understanding of the enzymes and transporter proteins that determine the disposition of drugs in the body, scientists can make more accurate extrapolations from in vitro data to an in vivo situation.

For example, in vitro data can contribute to the prediction of clinical parameters for new compounds before and during clinical development and can also be used to better predict inter-individual variations in drug response, supporting personalized medicine concepts.

To reflect this scientific progress, the FDA, EMA and Japan’s MHLW (PMDA) have issued updated draft guidance/guidelines ^{[1, 2, 3]} outlining what in vitro data should be obtained and how this should be processed with possible consequences for potential clinical assessment and labeling. A summary of the development of PMDA DDI assessment in Japan has recently been published.⁴

The timeline below outlines the development of FDA, EMA and PMDA guidance in this area.

How DDI regulatory guidance has changed over time.

A more harmonized approach to DDI assessment from regulatory agencies

All major regulatory authorities (FDA, EMA, PMDA) reassuringly recommend a similar approach to DDI testing although minor differences, particularly for transporters and CYP induction, are apparent.

Furthermore the guidance is not overly prescriptive on the exact experimental testing needed and welcomes a variety of methodological approaches, provided they are based on well-validated and scientifically-sound models, particularly with respect to transporters.

The major change with recent FDA and PMDA guidance, which aligns with the EMA, was to use unbound concentrations of investigational drug concentrations (not total drug) for the calculation of DDI R cut-off values.

This change makes regulatory submissions easier by providing maximum flexibility for the methods used during DDI assessment. This flexibility is especially important for drug transporter interaction assessments, where scientific understanding is growing rapidly and assay validations are still in development. However some regulatory areas are not yet fully clarified across agencies. For instance, there is not any specific cut-off for poorly soluble drugs in the evaluation of inhibition and induction of enzymes or any specific guidance for UGT enzymes or some gut interactions.

Understanding victim & perpetrator drug testing classifications

In drug-drug interaction assessments, drugs are classified as either ‘victim’ drugs (drugs that are influenced by other drugs) or ‘perpetrator’ drugs (drugs that cause an alteration in the metabolism/PKs of victim drugs).

The regulatory guidance suggests various models to test drugs as both victim and perpetrator, but the following are the most common models used to inform the decision-making process:

A basic model, using in vitro tests
A more dynamic model, using PBPK modeling

Dynamic PBPK modeling has advantages over the basic model because it is easier to assess and investigate the magnitude and complexity of interaction(s).

DDI in vitro assessments are needed earlier in clinical development

In their 2020 guidance, the FDA recommends that in vitro DDI assessments be conducted before clinical testing starts, now forming part of the IND/CTA-enabling program.

Because in vitro DDI experiments highlight the potential impact of multiple clearance pathways, particularly if one is shut down by a perpetrator co-medication, an earlier assessment will lead to the identification of better clinical candidates and reduce the risk of clinically relevant DDIs.

This timeline shrinkage is especially important in therapeutic areas such as oncology where there is a vital requirement to move into clinical studies rapidly since patients will likely be on concomitant medications.

Overall, evaluating the DDI potential of an investigational new drug (IND) involves:

identifying the principal route(s) of elimination for the NCE
estimating the contribution of metabolizing enzymes and transporters to clearance of the NCE
characterizing the effect of the NCE on enzymes and transporters

Ultimately, in vitro data aids decision making as to whether a clinical DDI study in human volunteers is warranted for an investigational drug. It can guide the design of clinical interaction studies in human volunteers and assist product labeling versus other possible concomitant drugs.

Guidance on conducting clinical DDI studies is provided in the January 2020 FDA guidance: Clinical Drug Interaction Studies — Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry.⁵

Together, the in vitro and clinical guidance documents describe a systematic, risk-based approach to assessing the DDI potential of investigational drugs and making recommendations to mitigate DDIs.

Guidance for testing metabolites

The FDA, EMA and PMDA guidance(s) now require major metabolites of parent drugs (or those that contribute significantly to pharmacological activity or contain structural alerts for known DDI mechanisms) to be assessed for DDI potential.

The EMA also recommends assessment of the contribution from definitive in vivo data (metabolites that represent >10% of drug-related exposure observed during the ¹⁴C human AME mass balance study).

Generally, for metabolites, an in vitro CYP enzyme and/or transporter inhibition study is recommended:

if the metabolite is less polar than the parent drug and the AUC_metabolite ≥25% of AUC_parent

if the metabolite is more polar than the parent dug and the AUC_metabolite ≥AUC_parent.

for metabolites with structural alerts for time-dependent inhibition (TDI)

When investigating the inhibitory effect of a metabolite, the concentration should be set in a range including 50 × C_max (unbound concentration) of the metabolite, similarly the case for the unchanged drug.

DDI Testing as a Victim Drug

Reaction Phenotyping: Testing as a substrate for drug metabolizing enzymes and transporters

Reaction phenotyping studies can help explore the extent to which a drug is being victimized. Many historic drug withdrawals that have helped to shape the pharma industry today–for example terfenadine and astemizole –have been made because of their susceptibility to drug-drug interactions as a victim–not as a perpetrator! A new drug that has a high fraction metabolized (F_m, indicating a single enzyme dominating clearance) with a narrow therapeutic window will be at risk of non-approval.

A DDI assessment generally begins with the use of basic experimental modeling, and the major approaches used for assessment of victim drug potential are discussed below.

In general, it is the view of all agencies that a drug that is metabolized by multiple enzymes has a lower DDI potential than a drug that is metabolized by a single enzyme. Multiple metabolic pathways reduce the likelihood of DDIs as the closure of one metabolic pathway (e.g. by concomitant medicines) is unlikely to result in complete loss of the elimination pathway.

In addition, understanding the enzymology of a drug allows a greater assessment of potential DDIs and inter-individual variations in drug efficacy if, for example, polymorphic or inducible enzymes are involved.

Reaction phenotyping studies should be performed to identify the major drug metabolizing enzymes involved in the formation of metabolites. Such enzymes include phase I enzymes CYP (including news ones such as CYP2J2 and 4F2), flavin mono-oxygenase (FMO), aldehyde oxidase, monoamine oxidase (MAO), carboxyl esterase and alcohol/aldehyde dehydrogenase (ALDH) among others. Conjugate metabolite mapping must also be conducted, including UGT and sulfo-transferases (SULT).

Regulatory Guidance Comparison

FDA 2020

The FDA recommends that companies identify which enzyme(s) is responsible for contributing to ≥25% of a drug’s overall elimination, using in vitro basic models and, if available, human PK.

EMA 2013

In general, enzymes involved in metabolic pathways estimated to contribute to ≥25% of drug elimination should be identified, if possible, and the in vivo contribution quantified. This applies to both CYP enzymes and non-CYP enzymes.

PMDA 2018

If it is presumed from the results of in vitro metabolism studies and clinical PK studies that a particular enzyme contributes by at least 25% in the total elimination of the investigational drug, then the enzyme(s) involved should be identified.

Drug Transporters

Over the past ten years drug-drug interactions involving transporter proteins (rather than metabolizing enzymes) have been increasingly identified, and the development of in vitro transporter assays have become an area of active research for DMPK groups.

Efflux transporters such as P-gp, BCRP and MRP tend to efflux drug metabolites from the inside to the outside of the cell. Uptake transporters such as OATP tend to influx drugs into cells.

At the moment, all regulatory authorities recommend using an appropriate in vitro model such as Caco-2 cells, transfected cell lines and/or vesicles/membranes to explore these potential interactions. At the same time, though, the agencies also recognize that the field of transporter interaction assessments is still rapidly developing. Therefore they suggest that for transporters where ideal in vitro models are still developing, the latest scientific advice and internal laboratory validations be followed and developed.

Regulatory Guidance Comparison

FDA 2020	EMA 2013	PMDA 2018
As substrate: Test for: P-gp, BCRP, OATP1B1, OATP1B3, OCT2, OAT1, OAT3, MATE1, MATE-2K All drugs: test for P-gp and BCRP. Other transporters, as deemed appropriate based upon dominant clearance mechanisms. P-gp and BCRP positive as substrate if: A net flux ratio (or efflux ratio (ER)) of ≥2 for an investigational drug in cells that express P-gp (e.g. Caco-2 cells or transfected cells overexpressing P-gp) • A flux that is inhibited by at least one known P-gp inhibitor at a concentration at least 10 times its K_ior IC₅₀	As substrate: Test for: P-gp and BCRP Positive as substrate if: ER in Caco-2 cells is >2ER can be inhibited by a recognized P-gp/BCRP inhibitor	As substrate: Test for: P-gp and BCRP Positive as substrate if: ER in Caco-2 OR transfected cells is >2ER can be inhibited by a recognized P-gp/BCRP inhibitor
OATP1B1, OATP1B3: An investigational drug is considered an in vitro substrate for OATP1B1 or OATP1B3 if: the uptake of the drug in OATP1B1- or OATP1B3-transfected cells is ≥2-fold of the drug’s uptake in empty vector-transfected cells AND a known inhibitor can decrease the drug’s uptake to ≤50% at a concentration at least 10 times that of the K_i or IC₅₀.	OATP1B1 and OATP1B3: For drugs with estimated hepatic elimination (total hepatic metabolism + biliary secretion) of >25% of systemic clearance. No specific method is suggested, but it is assumed that uptake studies with transfected cell lines is appropriate in accordance with other agencies.	OATP1B1 and OATP1B3: Human hepatocytes whose OATP1B1 and/or OATP1B3 transport activity has been confirmed should be used. The investigational drug is judged as a substrate of OATP1B1 and OATP1B3 when there is an uptake of the typical substrates and inhibition by typical inhibitors to the extent that can be theoretically estimated by the concentration of the added inhibitor and the K_i value. Cell lines expressing OATP1B1 and OATP1B3 can be used. The uptake of the investigational drug should be determined using cells in which the ratio of the uptake in the typical substrate in transporter-expressing cell line to that in a non-expressing cell line (uptake ratio) has been confirmed to be not less than 2-fold, and that uptake is inhibited by known inhibitors to the extent that can be theoretically estimated by the concentration of the added inhibitor and the K_ivalue. Under this condition, when the same condition as the above-described typical substrate is satisfied for the investigational drug, the investigational drug is judged as a substrate of OATB1B1 and OATP1B3.
OCT2, OAT1, OAT3, MATE1, MATE-2K: The investigational drug is an in vitro substrate for the above transporters if: the ratio of the investigational drug’s uptake in the cells expressing the transporter versus the drug’s uptake in control cells (or cells containing an empty vector) is ≥2 AND a known inhibitor of the transporter decreases the drug’s uptake to ≤50% at a concentration at least 10 times its K_i or IC₅₀. positive as substrate if uptake in transfected cells is >2 fold compared to control (vector control) and can be inhibited by recognized inhibitors	OCT2, OAT1, OAT3, MATE1, MATE-2K: As deemed appropriate using uptake studies in relevant transfected cell lines based upon dominant clearance mechanisms. Although not specifically listed in EMA 2013 guidance due to the publication year, it is now advisable to conduct MATE testing.	OCT2, OAT1, OAT3, MATE1, MATE-2K: Cell lines as above for OATP transporters. *Note acidification should be considered for MATE-mediated transport.

DDI Testing as a Perpetrator Drug

Inhibition: Testing as inhibitor of drug metabolizing enzymes and transporters

Generally, a perpetrator DDI assessment begins with the use of basic experimental modeling and the major approaches used are discussed below.

Cytochrome P450 Inhibition

Cytochrome P450 enzymes metabolize approximately 50% of small molecule medications on the market today and, as such, are a major focus of DDI assessments. Inhibition of CYP450 is a major cause of PK-based DDIs, and drugs that are inhibitors of CYP450 enzymes (perpetrator drugs) reduce the clearance of other drugs that are metabolized by inhibited enzymes (victim drugs).

All agencies (FDA, EMA & PDMA) recommend that the inhibition of CYP450 enzymes be assessed using in vitro methods and the agencies suggest a similar study design:

Use a panel of [agency] recommended CYP450-selective chemical substrates and inhibitors to assess individual CYP450 enzymes.
Investigate CYP450 enzymes where clinical relevance has been shown or is understood (currently these include CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6 and 3A4).
Determine any inhibition constant, known as K_i for reversible inhibitors and K_I/_kinact (inhibition constant and maximal rate of inactivation) for mechanism-based inhibitors.

It is worth noting that all agencies now recommend that unbound concentrations (in human liver microsomes) of an inhibitor be used to derive the inhibition constant K_i.

Regulatory authorities also recommend the same approach be used for assessing time-dependent inhibition (TDI), an increasingly important component of DDI testing. TDI is characterized by an increase in inhibitory potency (often IC₅₀) following a period of metabolic activation (typically 30 minutes).

TDI is used as a marker of potential mechanism-based inhibition (MBI), which can be of more concern than reversible inhibition as MBI can be irreversible. Therefore the inhibitory effect of MBI is generally of a longer duration than with reversible inhibitors, and the body must synthesize additional CYP proteins to overcome the inhibition. Therefore, the risk assessment of an investigational drug is different for reversible and mechanism-based (irreversible) inhibitors and will require different approaches when assessed clinically.

CYP inhibition is fully aligned across all three agencies and all utilize the ratio of intrinsic clearance by the metabolizing enzyme in the absence and presence of an inhibitor – this is called the R value.

Regulatory Guidance Comparison

FDA 2020	EMA 2013	PMDA 2018
Liver inhibition Reversible inhibitor: R1 = 1 + [I]/K_i ≥1.02 Where: [I] = mean unbound C_max at the highest dose	Liver inhibition Reversible inhibitor: R1 = 1 + [I]/K_i ≥1.02 Where: [I] = mean unbound C_max at the highest dose	Liver inhibition Reversible inhibitor: R1 = 1 + [I]/K_i ≥1.02 Where: [I] = mean unbound C_maxat the highest dose
Gut inhibition 1+[I]_g/K_i ≥ 11 Where: [I]_g = dose/250 mL	Gut inhibition 1+[I]_g/K_i ≥ 11 Where: [I]_g = dose/250 mL	Gut inhibition 1+[I]_g/K_i ≥ 11 Where: [I]_g = dose/250 mL
Time-dependent inhibition Liver R2 = (k_obs + k_deg)/k_deg Where: k_obs = (k_inact × 50 × _Imax,u)/(K_I,u + 50 × I_max,u)R2 = 1 + {k_inact x [I]/(K_I + [I])}/k_deg 1.25 Where: [I] = unbound C_max x 50	Time-dependent inhibition Liver R = 1 + {_kinact x [I]/(K_I + [I])}/k_deg ≥1.25 Where: [I] = unbound C_max x 50	Time-dependent inhibition Liver R = 1 + {k_inact x [I]/(K_I + [I])}/k_deg ≥1.25 Where: [I] = unbound C_max x 50
Gut R2 = 1 + {k_inact x [I]/(K_I + [I])}/k_deg ≥1.25 Where: [I] = 0.1 x dose/250 mL	Gut R = 1 + {k_inact x [I]/(K_I+ [I])}/k_deg ≥1.25 Where: [I] = 0.1 x dose/250 mL	Gut R = 1 + {k_inact x [I]/(K_I + [I])}/k_deg ≥1.25 Where: [I] = 0.1 x dose/250 mL

For transporters, all regulatory authorities recommend using an appropriate in vitro model, such as Caco-2 cells, transfected cell lines and/or vesicles/membranes. Sometimes for specific applications primary human cells such as hepatocytes and renal cells can be used to test for inhibitory interactions.

Regulatory Guidance Comparison

FDA 2020	EMA 2013	PMDA 2018
As inhibitor: All drugs: P-gp, BCRP, OATP1B1, OATP1B3, OCT2, OAT1, OAT3, MATE1, MATE-2K The investigational drug has the potential to inhibit P-gp or BCRP in vivo if the investigational drug is administered orally, and: I_gut /IC₅₀ or K_i ≥10 Where: I_gut = dose of inhibitor/250 mL Where: [I]1 = mean steady state total (free and bound) C_max [I]2 = dose (mol)/250 mL	As inhibitor: All drugs: P-gp, BCRP, OATP1B1, OATP1B3, OCT2, OAT1, OAT3, and preferably BSEP Intestinal transporters: e.g. P-gp: K_i ≤0.1 x max dose/250 mL	As inhibitor: All drugs: P-gp, BCRP, OATP1B1, OATP1B3, OCT2, OAT1, OAT3, MATE1, MATE-2K P-gp and BCRP Measure ER of probe substrates in relevant cell line, if inhibition by test is observed and considered positive if: [I]/IC₅₀ >10 Where: [I] = maximum dose/250mL
OATP1B1 and OATP1B3 The investigational drug has the potential to inhibit OATP1B1/3 in vivo if the R value is >1.1: R=1+ ((f_u,p × I_in,max)/IC₅₀) ≥1.1 Where: f_u,p is the unbound fraction in plasma IC₅₀is the half-maximal inhibitory concentration. I_in,max is the estimated maximum plasma inhibitor concentration at the inlet to the liver With OATP transporters, adding a pre-incubation phase is recommended as some drugs have been shown to be time-dependent inhibitors of these uptake transporters	Hepatic uptake after oral administration: e.g. OATP1B1/1B3: K_i ≤25 x unbound maximum concentration entering the liver (I,_max,u)	OATP1B1 and OATP1B3: Measure uptake of probe substrates in relevant cell line (validated by use of control inhibitors), if inhibition by test is observed, considered positive if: 1+f,_u,b x I_inlet /K_i ≥1.1
OCT2, OAT1, OAT3, MATE1, MATE2-K: The investigational drug has the potential to inhibit these transporters in vivo if the I_max,u/IC₅₀ value is ≥0.1	Renal transporters hepatic efflux and uptake (OAT1/OAT3, OCT2, MATE1, MATE2-K: K_i ≤50 x unbound C_max Although not specifically listed in EMA 2013 guidance due to the publication year, it is now advisable to conduct MATE testing.	OCT2, OAT1, OAT3, MATE1, MATE2-K: Measure uptake of probe substrates in relevant cell line (validated by use of control inhibitors), if inhibition by test is observed, considered positive if: OCT2, OAT1, OAT3:1+(unbound C_max)/K_i ≥1.1 MATE1, MATE2-K: 1+(unbound C_max)/K_i ≥1.02

CYP Induction

The most significant regulatory changes have happened in the CYP450 induction area. Compared to previous versions of DDI guidance, the publication of the FDAs current guidance in 2020 has brought further harmonization between the agencies.

DDIs from enzyme induction tend to be of a lesser magnitude than those observed with CYP inhibition (that is, a lower plasma AUC shift of victim drugs is generally observed). However, enzyme induction can lead to decreased efficacy and/or increased formation of toxic metabolites.

All agencies now recommend that CYP induction be assessed using cultured human hepatocytes from multiple donors (n = 3 recommended) with an initial cytotoxicity test to help define working drug concentrations.

All regulatory authorities recommend testing for CYP1A2, 2B6 and 3A4 induction, at least in the first instance. The FDA suggest that if CYP3A4 induction is observed, then induction of CYP2C sub-family should also be assessed (i.e. CYP2C8, 2C9 and 2C19) because they are induced via the same nuclear receptor (PXR). However, in addition to CYP3A4 (via PXR activation), it may also be advisable to assess CYP2C induction if CYP2B6 induction is observed, as the induction of CYP2C enzymes can also be initiated by cross-talk activation of the CAR receptor which initiates CYP2B6 induction.

Prototypical (human) cytochrome P450 inducing agents, such as omeprazole (induces CYP1A) and rifampicin (induces CYP2C and CYP3A) should be included as controls to help rationalize the potency of any inductive effect(s) observed with the test compound(s).

Another change from previous guidance is that the sole index of induction can be based on a simple increase in levels of mRNA (assessed using real-time qPCR), as long as certain control values are met. Although measurement of the catalytic activity of the CYP enzymes is allowed, analysis of mRNA is still often preferred because the mRNA analysis is a more sensitive technique, and the possibility of a false-negative is minimized if the test compound is a metabolism-dependent CYP inhibitor. The EMA also recommend the measurement of free drug concentrations during the incubation phases on each day.

Regulatory Guidance Comparison

Generally, there are three main ways CYP induction is assessed:

Fold-change method in mRNA levels
Correlation and calibration with positive and negative controls
R-value calculation.

FDA 2020	EMA 2013	PMDA 2018
Fold change method: RNA ≥2-fold vs. solvent control and is concentration-dependent mRNA ≤ 2-fold but ≥ 20% of positive control	Fold change method: mRNA ≥ 2-fold vs solvent control and is concentration-dependent mRNA ≤ 2-fold but ≥ 20% of positive control	Fold change method: mRNA ≥ 2-fold vs solvent control and is concentration-dependent mRNA ≤ 2-fold but ≥ 20% of positive control
Correlation method: *Method 1* Calculate a relative induction score (RIS) using: (E_max × I_max,u)/(EC₅₀ + I_max,u) ≤ 0.8 *Method 2* Calculate: I_max,u / EC₅₀ values ≤0.8	Correlation method: Predicted positive criteria defined by known positive controls can be used RIS: E_max x [I]/(EC₅₀+[I]) When: [I] = unbound C_max	Correlation method: Not specified
R-value method: R3 = 1/[1 + d × ((E_max × 10 × I_max,u)/(EC₅₀ + 10 × I_max,u))] ≤0.8 When: d is the scaling factor and is assumed to be 1. A different value can be used if supported by prior experience with the system used E_max is the maximum induction effect determined in vitro I_max,u is the maximal unbound plasma concentration of the interacting drug at steady state* EC₅₀ is the concentration causing half-maximal effect determined in vitro *Considering uncertainties in the protein binding measurements, the unbound fraction should be set to 1% if experimentally determined to be <1%	R-value method: Not specified	R-value method: Not specified

In Silico Modeling

Increasingly, Model-Informed Drug Development (MIDD) is incorporated during early candidate development to assist in strategic decision-making. Exposure-based biological and statistical models are developed using preclinical and/or clinical data sources.

In many cases, these models can also be used to predict data that cannot be generated readily experimentally. The FDA has increasingly stated their interest in integrating MIDD into development programs and there has been a dramatic increase in the number of submission packages (IND and NDA) that have utilized MIDD in their development programs in the period from 2008 to 2017 (>250).⁶

For DDI evaluation, PBPK models can be used to predict test item interactions with other drug(s) based on in vitro and in vivo data inputs. The model will help evaluate the impact of interacting drugs on the exposure of test article and vice versa, as applicable.

In addition, MIDD can be scaled for other populations (disease, ethnicity, genotype age, etc.). Crucially model parameters can be updated and evolved for the target population of interest, and simulations can be provided to support modification of dosing regimens.

Conclusion

In vitro science has advanced greatly over the past 10 years assisted by an increasingly sophisticated array of models and instrumentation. We now have at our disposal immortalized cell lines that are transfected with enzymes or transporter proteins (including double transfections), bio-generation of an increasing range of enzymes using mammalian cell or bacterial vectors (amongst others), cryopreserved hepatocytes that will attach for culture and human liver microsomes pooled from over 200 donor livers. We also have high-pressure liquid chromatography and fast-scanning MS/MS instrumentation ever pushing the limits of detection to far lower levels.

All these factors, in conjunction with powerful in silico modeling now allow scientists to make more accurate predictions about certain kinds of DDIs in vivo and to try and control these by appropriate labeling. As a result, the kinds of interactions that caused high-profile drug withdrawals during the late 1990s (e.g. terfenadine as a victim drug and mibefradil as a perpetrator drug) can now be largely predicted in the laboratory from in vitro data, and it is unlikely that this type of drug withdrawal (based upon inhibition of CYP450 enzymes) will happen again.

The regulatory guidance now available in this area provides a solid framework when considering the in vitro information required to assess the DDI potential of a new chemical entity.

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References

[1] In Vitro Drug Interaction Drug Interaction Studies – Cytochrome P450 Enzyme and Transporter Mediated Drug Interactions Final – Center for Drug Evaluation and Research (CDER), Food and Drug Administration (FDA), Rockville, MD, January 2020

[2] Guideline on the Investigation of Drug Interactions, European Medicines Agency, 21 June 2012, CPMP/EWP/560/95/Rev. 1 Corr. 2**, Committee for Human Medicinal Products (CHMP) – finalized 2013.

[3] Ministry of Labor and Welfare. Guideline on drug interaction for drug development and appropriate provision of information, notification No.0723-4, pharmaceutical evaluation division, pharmaceuticals safety and environmental Health bureau, Japan. July 23, 2018. https://www.pmda.go.jp/files/000228122.pdf.

[4] Development of a new Japanese guideline on drug interaction for drug development and appropriate provision of information, Pharmaceuticals and Medical Devices Agency, Chiyoda-ku, Tokyo, 100-0013, Japan https://doi.org/10.1016/j.dmpk.2019.11.009; 1347-4367/2020 The Japanese Society for the Study of Xenobiotics.

[5] Clinical Drug Interaction Studies —Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions, Guidance for Industry, U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), January 2020, Clinical Pharmacology

[6] Physiologically Based Pharmacokinetic Modeling in Regulatory Science: An Update From the U.S. Food and Drug Administration’s Office of Clinical Pharmacology.J Pharm Sci. 2019 Jan; 108(1), 21-25

Abbreviations

ALDH = Aldehyde dehydrogenase
AME = Absorption, Metabolism and Excretion
ADR = Adverse drug reaction
AUC = Area Under the Curve
BCRP = Breast Cancer Resistance Protein
CYP = Cytochrome P450
DDI = Drug-drug interaction
EMA = European Medicines Agency
FDA = U.S. Food and Drug Administration
FMO = Flavin monooxygenase
IC₅₀ = Inhibitory concentration that causes 50% inhibition
IND = Investigational new drug
JMHLW = Japanese Ministry of Health, Labour and Welfare
ITC = Internal transporter consortium
MAO = Monoamine oxidase
MBI = Mechanism-based Inhibition
MIDD = Model-informed drug development
MRP = Multi-resistance protein
MS = Mass spectrometry
NCE = New chemical entity
NDA = Nuclear Decommissioning Authority
OATP = Organic Anion Transporter Protein
PBPK = Physiological based Pharmacokinetic
PK = Pharmacokinetic
PMDA = Pharmaceuticals and Medical Devices Agency
qPCR = Quantitative polymerase chain reaction
SULT = Sulfo-transferases
TDI = Time dependent inhibition
UGT = Uridine di-phospho glucuronosyl transferase

A comparison of FDA, EMA & PMDA regulatory guidance for in vitro drug-drug interaction (DDI) assessments

The Evolving Regulatory Landscape

A more harmonized approach to DDI assessment from regulatory agencies

Understanding victim & perpetrator drug testing classifications