Focus of this presentation is Different types of drug drug interaction studies in Humans with examples and FDA guidance for industry for such studies.

1. Drug-Drug Interaction Studies
in Humans
Dr. Govind Mishra, MD
DM Resident (Clinical Pharmacology)
AIIMS Bhubaneswar

2. Outline
• Introduction
• Factors affecting drug exposure and response
• Goals of a DDI program
• Timing of DDI evaluation
• Types of DDI studies
• Properties of index
substrates/inducers/inhibitors
• DDI studies involving drug transporters
• Pitfalls
• Conclusion

3. Introduction
• Unintentional and mismanaged drug–drug interactions (DDIs) are a common
reason for preventable adverse events.
• About 10–20% of ADRs may be associated with a DDI
• Polypharmacotherapy being more common, there is an increased likelihood of
DDIs that can lead to exaggeration of adverse effects or loss of drug efficacy.
• Multiple market withdrawals- such as those of mibefradil, terfenadine, cisapride,
and cerivastatin in the late 1990s and early 2000s.

4. Mullins ME, Horowitz BZ, Linden DH, Smith GW, Norton RL, Stump J. Life-threatening
interaction of mibefradil and beta-blockers with dihydropyridine calcium channel
blockers. JAMA. 1998 Jul 8;280(2):157-8.
JUNE
1998

5. JAN
1997

6. JULY
2000
Thomas AR, Chan LN, Bauman JL, Olopade CO.
Prolongation of the QT interval related to cisapride-
diltiazem interaction. Pharmacotherapy. 1998 Mar-
Apr;18(2):381-5.

7. AUG
2001

8. Introduction cont…..
• A major proportion of harmful drug interactions is based on alterations of the
plasma concentrations of the victim drug due to the perpetrator drug causing a
change in the metabolism or transporter-mediated disposition of the victim
drug.
• Inhibition of drug metabolism or transporter-dependent elimination in most
cases leads to elevated concentrations of the victim drug.
• Induction increases metabolic elimination, decreasing the concentrations of the
victim.
• In the worst case, such interactions can lead to several hundred-fold variations
in drug exposure.

9. Factors affecting Drug exposure and response
Ultimate Goal
Optimal Dosing for
patients with these
individual factors
Critical step
Evaluate how these
factors affect drug
exposure and
response
Adapted from: Huang SM, Temple R. Is this the drug or dose for you? Impact and
consideration of ethnic factors in global drug development, regulatory review,
and clinical practice. Clin Pharmacol Ther. 2008 Sep;84(3):287-94

10. Goals of a DDI program during drug development
Determine the following :
• Whether the investigational drug alters the PK of other drugs.
• Whether other drugs alter the PK of the investigational drug.
• The magnitude of changes in the PK parameters.
• The clinical significance of the observed DDIs.
• The appropriate management strategies for clinically significant DDIs.

11. Timing of DDI evaluations
• Early – in-vitro evaluations
Screen for DDI potential
• Determine timing of clinical DDI studies relative to other studies in
development program.
• Assess clinical DDIs before the product is administered to patients likely to
take medications that could interact
- Reduce exclusion criteria in clinical trials.

12. Investigation of Drug- Drug interaction
Adapted from: Tornio A, Filppula AM, Niemi M, Backman JT. Clinical Studies on
Drug-Drug Interactions Involving Metabolism and Transport: Methodology,
Pitfalls, and Interpretation. Clin Pharmacol Ther. 2019 Jun;105(6):1345-1361.

13. Types of DDI studies
• Prospective and Retrospective studies
• Standalone and Nested studies
• Index studies
• Concomitant use studies
• In silico studies

14. Prospective
&
Retrospective
• Prospective
– Protocol includes DDI objective
– Specifically designed to detect or quantify DDI
– Stand-alone or nested
• Retrospective
– No DDI objective in protocol
– Results may be difficult to interpret

15. Gebretsadik Z, Gebrehans M, Getnet D,
Gebrie D, Alema T, Belay YB. Assessment of
Drug-Drug Interaction in Ayder
Comprehensive Specialized Hospital, Mekelle,
Northern Ethiopia: A Retrospective Study.
Biomed Res Int. 2017;2017:9792363.

16. Stand-alone
&
Nested
• Stand-alone study
- main objective is DDI evaluation
• Nested study
- Prespecified analysis within a larger study
(ex: phase 3 study)

17. Index Studies
• Use perpetrators or substrates with well defined properties (level of inhibition,
induction, and metabolic pathway)
– Investigate drug as substrate: Use index inhibitors and inducers (strong = worst case)
– Investigate drug as inhibitor or inducer: Use index substrate (sensitive=worst case)
• May not be clinically relevant for intended patient population
• Extrapolate to other substrates and perpetrators
• Inform need for additional DDI studies

18. Terminology
• Based on the effect on a sensitive index CYP substrate
– Strong inhibitor: increases the AUC ≥ 5-fold
– Moderate inhibitor: increases the AUC ≥ 2- to < 5-fold
– Weak inhibitor: increases the AUC ≥ 1.25- to < 2-fold
– Strong inducer: decreases the AUC ≥ 80 percent
– Moderate inducer: decreases the AUC ≥ 50 to < 80 percent
– Weak inducer: decreases the AUC ≥ 20 to < 50 percent
• Based on the effect of a strong index inhibitor
– Sensitive substrate: AUC is increased ≥ 5-fold
– Moderate sensitive substrate: AUC is increased ≥ 2- to < 5-fold

19. Index inhibitors of CYP enzymes
Enzyme Inhibitor Strength Also inhibits
CYP1A2
Ciprofloxacin +++ CYP3A4
Fluvoxamine ++++ CYP2C10, 2D6, 3A4
CYP 2B6 Ticlopidine + CYP2C19
CYP2C8
Clopidogrel +++ CYP2B6,CYP2C19
Gemfibrozil ++++ OATP1B1
CYP2C9 Fluconazole ++ CYP2C19,3A4
CYP2C19 Fluconazole
Omeprazole
+++
++
CYP2C9,3A4
CYP2D6
Fluoxetine ++++ CYP2C19
Paroxetine ++++
Terbinafine +++ CYP1A2
CYP3A4 Itraconazole ++++ P-gp
Ritonavir
Erythromycin
++++
+++
P-gp
https://www.fda.gov/drugs/drug-interactions-labeling/drug-development-and-drug-interactions-table-
substrates-inhibitors-and-inducers#table2-1

20. Index Inducers of CYP enzymes
https://www.fda.gov/drugs/drug-interactions-labeling/drug-
development-and-drug-interactions-table-substrates-inhibitors-and-
inducers#table2-1
- CYP1A2: Phenytoin, Rifampin (moderate inducers)
– CYP2B6: rifampin (moderate inducer)
– CYP2C8: rifampin (moderate inducer)
– CYP2C9: rifampin (moderate inducer)
– CYP2C19: rifampin (Strong inducer)
– CYP3A: rifampin, phenytoin (Strong inducers)

21. Index substrates of CYP enzymes
Enzyme Substrate Sensitivity Other
enzymes/transporters
CYP1A2 Tizanidine ++++
Caffeine ++++ NAT,XO
Theophylline ++ CYP3A4,2E1
CYP2B6 Bupropion +
(S) Ketamine ++ CYP3A4
CYP2C8 Dasabuvir ++++ CYP3A4
Repaglinide +++ CYP3A4,OATP1B1
CYP2C9 (S) Warfarin +++
Tolbutamide +++
CYP2D6 Desipramine +++ CYP3A4
Nebivolol ++++ CYP2C19
CYP3A4 Midazolam ++++
Simvastatin ++++
https://www.fda.gov/drugs/drug-interactions-labeling/drug-development-and-drug-interactions-table-substrates-
inhibitors-and-inducers#table2-1

22. Transporter Substrates Inhibitors
BCRP Rosuvastatin Cyclosporine
Sulfasalazine Eltrombopag
OATP1B1 or OATP1B3 Atorvastatin Cyclosporine
Pitavastatin Rifampin (single dose)
Pravastatin
Repaglinide
Rosuvastatin
Simvastatin (acid)
OATP2B1 Celiprolol Apple, orange and grapefruit juices
(intestine)
OCT1 Sumatriptan N/A
Metformin
Transporter index substrates and inhibitors

23. Transporter Substrates Inhibitors
OCT2 Metformin Cimetidine
OAT1 Adefovir Pyrimethamine
Probenecid
OAT3 Benzylpenicillin Probenecid
P-gp Aliskiren Itraconazole
Dabigatran etexilate Verapamil
Digoxin
Fexofenadine
https://www.fda.gov/drugs/drug-interactions-labeling/drug-development-and-drug-interactions-table-substrates-
inhibitors-and-inducers#table2-1

24. (a) Effect of varying inhibitor dose (scenarios A-C) on
the plasma concentrations of a substrate drug. A
sufficient dose of the perpetrator drug is
necessary to reach strong inhibition and to detect
and accurately quantify a potential DDI.
(b) Effect of a cytochrome P450 (CYP) inhibitor on
the plasma concentrations of a substrate with
varying fractions metabolized by the affected CYP
(fm,CYP). In combination with a CYP inhibitor, a
substrate drug with a low fm,CYP will give a
smaller DDI than a sensitive substrate drug with a
high fm,CYP.
Adapted from: Tornio A, Filppula AM, Niemi M, Backman JT. Clinical
Studies on Drug-Drug Interactions Involving Metabolism and Transport:
Methodology, Pitfalls, and Interpretation. Clin Pharmacol Ther. 2019
Jun;105(6):1345-1361. doi: 10.1002/cpt.1435. Epub 2019 Apr 20

25. (c) Effect of dose staggering (scenarios A-D) on DDI
magnitude. Strongest DDI can be detected when the
victim drug is administered shortly after the inhibitor
drug.
(d) Effect of repeated dosing of perpetrator whose
metabolite inhibits the CYP enzyme (scenarios A-C)
on DDI magnitude. To study the worst-case scenario,
the perpetrator should be dosed to steady state and
victim drug given at the time of the peak
concentrations of the perpetrator.
Adapted from: Tornio A, Filppula AM, Niemi M, Backman JT. Clinical Studies on
Drug-Drug Interactions Involving Metabolism and Transport: Methodology,
Pitfalls, and Interpretation. Clin Pharmacol Ther. 2019 Jun;105(6):1345-1361.
doi: 10.1002/cpt.1435. Epub 2019 Apr 20

26. Noh YH, Lim HS, Jin SJ, Kim MJ, Kim YH, Sung HR, Choi HY, Bae KS. Effects of ketoconazole and rifampicin on the pharmacokinetics of gemigliptin, a
dipeptidyl peptidase-IV inhibitor: a crossover drug-drug interaction study in healthy male Korean volunteers. Clin Ther. 2012 May;34(5):1182-94

27. • In this select group of healthy male Korean volunteers, concurrent
administration of gemigliptin with ketoconazole or rifampicin was associated
with significantly increased or decreased systemic exposure to gemigliptin,
respectively.
• Ketoconazole - increased total gemigliptin plasma exposure (AUC0–; 2.36-fold
[90% CI, 2.19–2.54]) and decreased metabolism of gemigliptin.
• Pretreatment with rifampicin –
Decreased AUC0– of gemigliptin (by 80% [90% CI, 78%–82%]) and a 2.9-fold
increase (mean [SD], 0.18 [0.08] to 0.52 [0.10]) in the metabolic ratio of
gemigliptin.

28. Polepally, Akshanth R et al. “Assessment of Clinical Drug-Drug Interactions of Elagolix, a Gonadotropin-Releasing Hormone Receptor Antagonist.” Journal of
clinical pharmacology vol. 60,12 (2020): 1606-1616.

29. • As a victim of cytochrome P450 (CYPs) and transporter‐mediated DDIs, elagolix
area under the curve (AUC) increased by ∼2‐fold following coadministration with
ketoconazole and by ∼5‐ and ∼2‐fold with single and multiple doses of rifampin,
respectively.
• As a perpetrator, elagolix decreased midazolam AUC by 54% (50%‐59%) and
increased digoxin AUC by 32% (23%‐41%). Elagolix decreased rosuvastatin AUC
by 40% (29%‐50%).
• Dose adjustments.

30. Zahir H, Kobayashi F, Zamora C, Gajee R, Gordon MS, Babiker HM, Wang Q, Greenberg J, Wagner AJ. Evaluation of Potential Drug-Drug Interaction Risk of
Pexidartinib With Substrates of Cytochrome P450 and P-Glycoprotein. J Clin Pharmacol. 2021 Mar;61(3):298-306.

31. • Coadministration of single and multiple doses of pexidartinib resulted in
21% and 52% decreases, respectively, in the area under the plasma
concentration-time curve from time zero to the last measurable time point
(AUClast ) of midazolam (CYP3A4 substrate), whereas AUClast values of
tolbutamide (CYP 2C9 substrate) increased 15% and 36%, respectively.
• These results indicate that pexidartinib is a moderate inducer of CYP3A and a
weak inhibitor of CYP2C9.

32. Concomitant
use studies
• Drugs relevant to intended population
• Potential to interact (mechanism)
• May be difficult to extrapolate to other drug
pairs (or groups)
• Transporter-based drug-drug interaction
studies are often concomitant use studies
– No transporter index drugs have been
identified

33. Kang WY, Lee HW, Gwon MR, Cho
S, Shim WS, Lee KT, Yang DH,
Seong SJ, Yoon YR. A
Pharmacokinetic Drug
Interaction Between Fimasartan
and Linagliptin in Healthy
Volunteers. Drug Des Devel Ther.
2020 May 26;14:2101-2111.

35. • serial blood samples for the determination of plasma concentrations
were collected at the scheduled time points for fimasartan: 0 (pre-
dose), 0.25, 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 12, and 24 h after dosing on
days 7 and 14.
• Blood samples were obtained for linagliptin pharmacokinetics at 0
(predose), 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 6, 8, 10, 12, and 24 h after
dosing on days 7 and 14

36. This study consisted of two parts:
• Part A, an investigation of the effect of linagliptin on fimasartan was
performed and
• Part B, an exploration of the effect of fimasartan on linagliptin
There was no statistically significant difference in Ctrough values
between administration of the individual drug and concomitant
administration of both drugs.

37. In silico DDI studies
• Physiologically based pharmacokinetic (PBPK) models can replace some
clinical studies
• Examples:
– Impact of weak and moderate CYP2D6 and 3A4 inhibitors
– Impact of weak and moderate CYP3A4 inducers
• Verify model by comparing clinical and PBPK evaluation: effect of strong
perpetrator
• An evolving science
– New uses are being considered

38. In silico DDI
studies
Example

39. In-Silico DDI studies example
• Sonidegib capsules (Odomzo)- treatment of locally advanced basal cell
carcinoma
• CYP3A substrate
• Clinical DDI studies were conducted with strong CYP3A inhibitor
(ketoconazole) and strong CYP3A inducer (rifampin)
– with ketoconazole- AUC increased 2.2x; Cmax increased 1.5x
– with rifampin- AUC decreased 72%; Cmax decreased 54%

40. In-Silico DDI studies example
• PBPK
– With moderate inhibitor (erythromycin)- AUC would increase
1.8x (14d) and 2.8x (4 months)
– With moderate inducer (efavirenz)- AUC would decrease 56%
(14d) and 69% (4 months)

41. Considerations for transporter DDI studies
• FDA guidance instructs that the need of clinical transporter DDI studies of transporter
substrates identified in in vitro studies should be considered on the basis of the drug’s
site of action, route of elimination, likeliness of concomitant use, and safety issues.
• For example, in the cases of P-gp and BCRP-mediated DDIs, most clinically relevant DDIs
(aliskiren, dabigatran, digoxin, and fexofenadine as P-gp substrates, and rosuvastatin as
a BCRP substrate) are based on inhibition of the absorption limiting effects of these
transporters in the small intestine or, in some cases, biliary or renal excretion.

42. • For drugs targeting the liver or eliminated via the liver, current guidance
states that DDI studies focusing on OATP1B1 or OATP1B3 can be warranted.
• For drugs eliminated via renal excretion, studies on OAT1 or 3, OCT2, or
MATE-mediated DDIs can be needed.

43. Some challenges of transporter DDI studies
US Food and Drug Administration, Center for Drug Evaluation and Research. Clinical Pharmacology Biopharmaceutics
Review(s):https://www.accessdata.fda.gov/drugsatfda_docs/nda/2010/022512Orig1s000ClinPharmR_Corrrected%20
1. The lack of specific index substrates and inhibitors makes extrapolation of the
results of DDI studies with other drugs challenging.
For example, dabigatran etexilate could be used as a fairly specific P-gp substrate
because its intestinal absorption is limited by P-gp, but dabigatran elimination is not
dependent on renal P-gp and its sensitivity to P-gp inhibition at clinically used doses is
only modest, so that even the strongest P-gp inhibitors have increased its AUC less
than threefold.

44. Some challenges of transporter DDI studies
cont…
2. Well-established transporter inhibitors are typically either nonselective or cause
only limited extent of inhibition.
P-gp inhibitors, such as clarithromycin, itraconazole, quinidine,and verapamil, also
have inhibitory effects on CYP enzymes, and
Cyclosporine acts as a inhibitor of BCRP, P-gp, and OATPs, and also inhibits CYP3A4.

45. Hibma JE, Zur AA, Castro RA, et al. The Effect of Famotidine, a MATE1-Selective Inhibitor, on the Pharmacokinetics and Pharmacodynamics of
Metformin. Clin Pharmacokinet. 2016;55(6):711-721.

47. • The study highlights an effect of famotidine on metformin urinary excretion and
pharmacologic action, suggesting that the bioavailability and response to
metformin can be improved with a multidrug and toxin extrusion protein 1
(MATE1) inhibitor.
• Famotidine increased both metformin oral absorption and renal clearance to the
same extent, such that systemic exposure was unaltered, but elucidation of these
pharmacokinetic changes was possible only because both urinary metformin
recovery and renal clearance were obtained.

48. OCT/MATE transporters governing metformin hepatic
distribution and renal clearance

49. Study Planning: Stand-alone DDI studies
• Study Population- usually healthy volunteers, unless there are safety concerns
• Number of subjects- sufficient to detect a clinically significant DDI
• Dose
– Perpetrator- maximum dose
– Substrate- linear PK (any dose); dose dependent PK (therapeutic dose most likely
to interact)
• Single or multiple dose
– Single dose perpetrator - if it is not a potential time dependent inhibitor or an
inducer and relevant concentrations are reached
– single dose substrate - if it is possible to extrapolate to clinical use.

50. Study Planning: Stand-alone DDI studies
• Parallel vs crossover- crossover preferred; parallel useful for long half-life drugs
• Timing of drug administration- typically administer at the same time
– consider staggered administration if perpetrator is an inhibitor of one
enz/transporter and inducer for another; different food conditions for drugs.
• Sample collection
– Adequate to characterize AUC, Cmax, (if relevant) Cmin

51. Study planning: Cocktail studies (a type of stand-alone study)
• Goal: simultaneously evaluate drug’s inhibition and induction potential for multiple CYPs and
transporters. (with or without prior in vitro studies)
• Cocktail criteria:
• Substrates are specific for individual CYP enzyme or transporter
• No interactions among the substrates
• Other study design criteria apply
• Results can be interpreted like other DDI studies, if design is appropriate

52. Isobologram analysis
Drugs with different mechanisms
of action are often used in
combination to achieve additive
and positive synergistic effects.
Such positive interactions of two
agents may permit use of reduced
concentrations of each drug,
thereby reducing concentration-
dependent adverse effects.

53. Zeidan A, Mazoit JX, Ali Abdullah M, Maaliki H, Ghattas T, Saifan A. Median effective dose (ED₅₀) of paracetamol and morphine for postoperative
pain: a study of interaction. Br J Anaesth. 2014 Jan;112(1):118-23.

54. This study aimed at defining the median effective analgesic dose
(ED50) of paracetamol, morphine, and the combination of both.
The ED50s of paracetamol, morphine and the combination were
2.1 g, 5 mg, and 1.3 g and 2.7 mg, respectively.
The isobolographic analysis showed that paracetamol and
morphine interact in an additive way.
The ED50 of paracetamol is too high and should not be given
alone to treat postoperative pain.

55. Sequence of dosing in the three groups,
Morphine (A),
Paracetamol (B), and
Morphine + Paracetamol (C)

56. Isobolographic
representation
of the
combination of
Morphine and
Paracetamol

57. Pitfalls of DDI studies
Typical requirements of a DDI study Potential pitfalls of DDI studies
General design issues
Healthy volunteers if no safety concerns Risky drugs given to healthy subjects
Sufficient washout to eliminate carry-over effects Insufficient washout (e.g., a slowly eliminated
metabolite still present)
Appropriate sample collection, storage and analytical
method to cover > 80–90% of the AUC of the victim
drug and metabolites
Inaccurate or insensitive analytical method, degradation
of analytes during storage or analysis
Monitoring of perpetrator pharmacokinetics
(compliance, quantification of exposure, presence after
washout)
Perpetrator exposure not documented
Pharmacodynamic assessment Pharmacodynamic assessments neglected

58. Typical requirements of a DDI study Potential pitfalls of DDI studies
Necessary prior knowledge considered in design Deficiencies in preclinical and early clinical data
Safety issues
Strict exclusion criteria (e.g., contraindications,
pregnancy)
Careless exclusion criteria leading to risk
of adverse effects
Safety monitoring and sufficient follow-up Insufficient follow-up (residual drug effects
Precautions and interventions to avoid adverse effects,
even in the worst-case DDI scenario
Rescue interventions not prearranged

59. Typical requirements of a DDI study Potential pitfalls of DDI studies
Perpetrator (inhibitor/inducer)
Selectivity and strength of index inhibitor (> 5-fold
increase in AUC possible)
Suboptimal strength, Nonselective inhibitor
Clinically relevant (high) dose depending on tolerability Too low dose, leading to weak inhibition
Dosing to reach and maintain steady-state, including time-
dependent inhibition and induction
Clinically atypical dosing Duration of dosing too short/long
to document maximal DD
Victim substrate
Sensitivity of index substrate Lack of sensitivity (particularly if not considered in
interpretation
Documented selectivity of index substrate Lack of selectivity (particularly if not considered in
interpretation)
High first-pass and short half-life preferable Long half-life victim with a short-acting or “presystemic”
inhibitor
Dose, expecting the worst-case scenario Too high dose
Staggered dosing Victim drug administered too soon (or too long)
after perpetrator to document maximal DDI

60. Bachmann K, White D, Jauregui L, Schwartz JI, Agrawal NG, Mazenko R, Larson PJ, Porras AG. An evaluation of the dose-dependent
inhibition of CYP1A2 by rofecoxib using theophylline as a CYP1A2 probe. J Clin Pharmacol. 2003 Oct;43(10):1082-90.
Rofecoxib was initially considered to be a moderate
inhibitor of CYP1A2. This information in the labeling was
mainly based on a DDI study using theophylline as a CYP1A2
index substrate, where rofecoxib 25 mg once daily increased
the AUC of theophylline only 1.5-fold.

61. Backman, Janne T et al. “Rofecoxib is a potent inhibitor of cytochrome P450 1A2: studies with tizanidine
and caffeine in healthy subjects.” British journal of clinical pharmacology vol. 62,3 (2006): 345-57.
• Rofecoxib increased the area under the plasma concentration–time curve (AUC0–∞) of tizanidine by
13.6-fold [95% confidence interval (CI) 8.0, 15.6; P < 0.001), peak plasma concentration (Cmax) by 6.1-
fold (4.8, 7.3; P < 0.001) and elimination halflife (t1/2) from 1.6 to 3.0 h (P < 0.001).
• Theophylline’s lack of sensitivity as an in vivo index substrate of CYP1A2.

62. • Grapefruit juice was
administered for 4 days in the
morning and 40 mg lovastatin
was administered in the
evening of day 3.
• Grapefruit juice increased
the AUC of lovastatin 1.9-fold
and that of lovastatin acid 1.6-
fold only. It is notable that the
exact dosing interval between
the perpetrator and victim is
not given in the paper
Rogers JD, Zhao J, Liu L, Amin RD, Gagliano KD, Porras AG, Blum RA,
Wilson MF, Stepanavage M, Vega JM. Grapefruit juice has minimal
effects on plasma concentrations of lovastatin-derived 3-hydroxy-3-
methylglutaryl coenzyme A reductase inhibitors. Clin Pharmacol Ther.
1999 Oct;66(4):358-66

63. Grapefruit juice increased
the AUC values of lovastatin
and lovastatin acid by 15-fold
and 5-fold, respectively.
In this study, grapefruit juice
was administered thrice daily
for 2 days, and lovastatin was
administered simultaneously
with grapefruit juice on day
3, followed by additional
grapefruit juice doses 0.5
and 1.5 hours after
lovastatin.
Kantola T, Kivistö KT, Neuvonen PJ. Grapefruit juice greatly increases serum
concentrations of lovastatin and lovastatin acid. Clin Pharmacol Ther. 1998
Apr;63(4):397-402.

64. Conclusion
• Evaluation of the DDI potential of drugs under
development and on the market is a crucial issue for drug
safety.
• Advances in in vitro methodology and modeling methods
have greatly advanced our understanding of molecular
mechanisms of DDIs and the ways to predict and interpret
them.
• Clinical DDI studies remain, however, an integral part of
the process of evaluating the DDI risks.
• Clinical studies in concert within silico models have great
synergy in providing comprehensive understanding of
DDIs and their clinical relevance.
• Key issues to consider when interpreting DDI data are the
strength and selectivity of the index perpetrator at the
given dose and the sensitivity of the index substrate, as
well as the general design of the study, including the dose
and time relationships.