Challenges and Opportunities with UGT Enzymes · • In vitro scaling methods • Simple allometric...

Challenges and Opportunities

with UGT Enzymes

Session: Reaction Phenotyping and Prediction of Human Clearance

of Non CYP-Mediated Pathways (#214)

AAPS Annual Meeting and Exposition 2015, Orlando, FL

Monday, 26th October 2015

Upendra Argikar

Analytical Sciences and Imaging

Novartis, Cambridge, MA

2 AAPS Annual Meeting 2015 / UGT / Upendra Argikar

Outline

I. CL prediction in drug discovery and early development

II. Introduction to UGTs

III. Challenges in CL estimation with UGT substrates

IV. Opportunities in CL estimation with UGT substrates

V. Common misconceptions

VI. Summary

VII. Acknowledgements


Drug discovery and development

Number

of

Compounds

Target and lead

Identification

Lead

Optimization

Candidate

Selection

Proof of

Concept

Information

on a NCE

of interest

NCE: New Chemical Entity

I


Importance of Clearance prediction

In discovery and early development, CL prediction -

• Helps to differentiate between promising clinical candidates

• Facilitates first-in-human studies

• Helps to project efficacious human doses

• Enables anticipation dosing frequency, safety margin, etc.

I


CL prediction methods: rodents, non-rodents to

human

• In vitro scaling methods

• Simple allometric scaling

• Interspecies scaling methods:

- single species scaling with and without correction of hepatic

blood flow

- single species proportionality

• Fraction (unbound) corrected intercept method (FCIM)

• Dedrick analysis

• Physiologically based PK modelling

• Micro-dosing in humans

Each with advantages, disadvantages and caveats

I


Metabolism

Renal

Bile

P450

UGT

esterase

FMO NAT MAO

Clearance Enzyme contribution UGT contribution

Williams JA, et al. DMD 2004 32:1201-1208

1A1

2B7

1A4

1A10

1A3

1A6 1A8

2B4

Clearance of the top 200 drugs in the US (2002) I


Enzymatic Drug Metabolism

Drug

(lipophilic, difficult to eliminate)

Metabolites

(hydrophilic, easier to eliminate)

Metabolism

(Biotransformation)

Phase I

functionalization

Phase II

conjugation

Introduce or unmask

Polar functional groups

(OH, COOH, NH2, etc)

• Add hydrophilic substituent to aid in

excretion.

•Metabolites are usually better substrates

of secretory transporters and are not as

easily reabsorbed

oxidation, reduction,

hydrolysis

glucuronidation,

sulfonylation, etc.

Phase 0 Phase III

II


UGTs: Uridine diphosphoglucuronosyl

transferases

Human UGTGene Family

UGT1

UGT2

UGT2A1

UGT2A2

UGT2A3

UGT1A9

UGT1A6

UGT1A1

UGT1A4

UGT1B8

UGT2B9

UGT1A5UGT1A3

UGT1A2p

UGT1A5

UGT1A8

UGT1A10

UGT2B10

UGT2B15

UGT2B7

UGT2B4

UGT2B11

UGT3UGT3A1

UGT3A2

UGT8UGT8A1

UGT2A

UGT2B

Modified from:

Burchell B, et al, 1998. Adv.

Pharmacol. 42:335-338.

Mackenzie PI, et al, 2005.

Pharmacogenet. Genomics.

15:677-685.

II


UGTs: Subcellular Localization

Modified from: Remmel RP., et al. In Drug Metabolism in Drug Discovery and Development. Eds: Humphreys GW, et al.

2007. Wiley.

II


General Mechanism of Glucuronidation Reaction

1. Dutton GJ. Glucuronic Acid - Free and Combined. 1966. Academic Press, New York and London.

2. Remmel RP., et al. In Handbook of Drug Metabolism. Eds: Pearson PG and Wienkers LC 2nd Edition. 2008.

Informa.

II


Glucuronidation reaction:

Compulsory ordered bi bi mechanism

Zhou J and Miners J. In Enzyme Kinetics in Drug Metabolism: Fundamentals and Applications. Eds: Nagar, S,

Argikar UA, Tweedie DJ. Springer/Humana Press.

II


In vivo

UGT expression (Link)

Species differences

Organ differences

Extrahepatic

glucuronidation

UGT polymorphisms

Induction and regulation

Challenges associated with CL prediction when

UGTs play a major role

Translation from in vitro models to

total in vivo CL is poor!

(clearance under-prediction)

In vitro enzyme kinetics

Phase II

Dependent on Phase 0 (and I)

May be rate limiting to Phase III

Overlapping substrates and lack of

specific inhibitors (Link)

Over expression in recombinant systems

Differences in preparation of cell lysates,

Supersomes, microsomes

Demanding syntheses for reference

standards

Elucidation of atypical kinetics is difficult

early on (Link)

Differences in incubation conditions (Link)

With or with BSA, HSA or FAIBP: Are fatty

acids involved? Which ones? (Link)

III


Opportunities and Possible solutions

1. Chemical entity based approaches

2. IVIVE: Relative activity factors (RAFs), CL calculation from r UGTs,

scaling and extrapolation

3. SAR/SPKR and IVIVE: CL delta for IVIVE (understanding fmUGT and

fmCYP)

IV

1. Chemical entity based

approaches

Example: Single Species Scaling Compound 1

IV


Compound 1: CL prediction, SS scaling

Based on CL data in monkey and

human on 132 compounds.

Lombardo FL, et al J. Clin. Pharm. 53(2) 178–191.

CL human = 0.40 * CL monkey

Tang H, Mayersohn M. Drug Metab Dispos. 2005;33(9):1297-1303.

Mahmood I. J Pharm Pharmacol. 1999;51(8):905-910.

Mahmood I, et al. J Clin Pharmacol. 2003;43(7):692-697.

Hosea N, et al. J Clin Pharmacol. 2009;49(5):513-33.

(Wt.human/Wt.animal)0.75

IV


Compound 1:

CL prediction, SS scaling based on monkey PK

• Similar findings by Deguchi et al (2011) for 12 marketed drugs.

(Deguchi T, et al 2011; DMD. 39(5) 820-829)

CL rat = 9 mL/min/kg

CL monkey = 5.7 mL/min/kg

CL human = 0.40 * 5.7 = 2.3 mL/min/kg

IV


Chemical entity based approach: Compound 1

Advantages

•Simple

•Based on relative differences

in physiological attributes

underlying drug disposition

differ across species (e.g., liver

weight, hepatic blood flow,

glomerular filtration rate).

•Extensive modelling (and data

generation) not needed

Disadvantages

•Coefficients derived for each

species should represent an

undefined amalgam of such

constants

•Non-mechanistic

•Large animal PK data needed

IV

2. IVIVE: Use of RAFs (Relative Activity Factors)

Compound 2

IV

for a known, specific UGT substrate


Proposed formation of M1 from Compound 2

Gunduz M, et al, 2010; DMD 38 (3) : 361 - 367.

Clu,int

(mL/min/mg)

Scaled CLh

(mL/min/kg)

Extraction

Ratio

HLM 32 12.6 0.63

IV


rUGT

(Supersomes®) M1

UGT1A1 +

UGT1A3 +

UGT1A4 -

UGT1A6 -

UGT1A7 -

UGT1A8 -

UGT1A9 -

UGT1A10 -

UGT2B4 -

UGT2B7 +

UGT2B15 -

UGT2B17 -

+ Detected

- Not Detected

Compound 2: Metabolism to M1

in liver microsomes across species

Gunduz M, et al, 2010; DMD 38 (3) : 361 - 367.

IV


Compound 2:

Clu,int

(mL/min

/mg)

HLM 32.2

rUGT1A1 3

rUGT1A3 21

rUGT2B7 5

Gibson CR et al, 2013; Xenobiotica 43 (12): 1027-1036

Liu H, et al, 2014; Biopharm Drug Dispos. 35: 513–524

E2: estradiol, CDCA: chenodeoxycholic acid, AZT: zidovudine

IV

for each specific UGT substrate

Substrate

for RAF

Relative

Activity

Factor

(RAF)

E2 Estradiol 1.6

CDCA 0.9

Zidovudine 1.8

Corrected

Clu,int

(mL/min

/mg)

Contribution

to overall

glucuronida-

tion

32.2

4.8 ~15%

18.9 ~59%

9 <30%


RAF based approach: Compound 2

Advantages

•Works great when glucuronide

reference standards are available

(Clint calculation) or when

glucuronidation is the predominant

pathway (ideally one metabolite)

•Semi-mechanistic, powerful when

cross checked with UGT protein

concentrations

•Applicable when interspecies

differences in rate/extent of

glucuronidation are observed

•Can be applied to other organs for

CL estimation.

•Large animal PK data not needed

Disadvantages

•UGT phenotyping needed

•If BSA is used, some UGTs -

1A9, 2B7 are shown to work

better in the presence of BSA

•Absence of specific UGT

substrates for calculation of

RAFs, i.e. other hepatic UGTs

may contribute

•1A3 for E2 (1A1)

•1A1, 2B7 for CDCA (1A3)

•2B4, 2B17 for AZT (2B7)

IV

3. Understanding CL delta:

fmCYP and fmUGT

approach

A series of compounds

IV


CL for NCEs within a chemical scaffold

• For 10 drugs on the market, CLint,u,CYP and Clint, u,UGT were additive,

when assessed under same conditions with respective cofactors.

Kilford PJ, et al, 2009; DMD 37: 82-89.

IV

UGTs: HLM, UDPGA CYPs: HLM, NADPH

Total: HLM, NADPH, UDPGA


fmCYP and fmUGT

: A powerful tool within a scaffold as a

chemical intervention strategy

Compound CLint, u, CYP

(uL/min/mg)

CLint, u, UGT

(uL/min/mg)

fmCYP fmUGT

#10 500 131 0.79 0.21

#11 749 234 0.76 0.24

#12 324 121 0.73 0.27

#13 543 98 0.85 0.15

#14 973 632 0.61 0.39

#15 954 432 0.69 0.31

#16 252 192 0.57 0.43

#17 874 478 0.65 0.35

#18 234 231 0.50 0.50

#19 252 176 0.59 0.41

#20 782 635 0.55 0.45

#21 334 351 0.49 0.51

#22 324 445 0.42 0.58

#23 485 786 0.38 0.62

#24 143 469 0.23 0.77

#25 123 569 0.18 0.82

IV


fmCYP and fmUGT

: A powerful tool within a scaffold as a

chemical intervention strategy

• And provides and estimate of contribution of glucuronidation to

scaled hepatic CL (from microsomes).

Gill KL, et al, 2012; DMD 40: 825 - 835

Cubbitt HE, et al, 2011; DMD 39: 864 - 873

Cubbitt HE et al, 2009; Pharm Res 26(5): 1073 - 1083

IV


fmCYP and fmUGT

approach

Advantages

•Semi-mechanistic.

•Applicable when interspecies

differences in rate/extent of

glucuronidation are observed

•Can be applied to other organs

for CL estimation, e.g. kidney

(1A9), intestine (1A8, 1A10), etc.

•UGT phenotyping not needed

•Large animal PK data not

needed.

•Can be applied to SULT

substrates: fmSULT

Disadvantages

•Laborious, esp. if fu, and Clint

with and without BSA are

estimated.

•Some UGTs - 1A9, 2B7 are

shown to work better in the

presence of BSA

• Works in HLMs, sequential

and concurrent metabolism in

hepatocytes acts as a limiting

factor.

IV


Misconceptions and Approaches that are not

recommended

1. Inhibition by UDP

• Shown only for UGTs 1A1, 1A4 in microsomes but not supported by

Supersomes, other kinetic drawbacks.

• Shown for UGT1A9 in sf9 cell lysate supernatants but nor reproduced in

Supersomes or microsomes yet.

• Cannot be generalized to all in vitro systems: differences in protein

expression, lipid compositions, activity, etc.

Fujiwara R. et al, 2008 DMD (36) 2: 361-367, Manveski N et al, 2012 DMD (40)11: 2192-2203.

2. Use of fu,p instead of fu,b in IVIVE

3. Use of total substrate concentrations instead of unbound substrate

concentrations when BSA or HSA or IFABP is utilized in incubations.

4. Michealis-Menten kinetics are absolute.

Need to check for atypical kinetics - homotropic/heterotropic

activation and comprehend the implications of in vitro atypical correlate.

V


Summary: CL prediction for glucuronidation

• There exist a multitude of challenges associated with

glucuronidation CL prediction / IVIVE.

• Currently applied techniques have progressed with time, although

none are close to perfect.

• Use of multiple approaches to gain confidence in the

predicted/extrapolated numbers for CL, when glucuronidation is the

predominant pathway.

• These approaches should not be used with the mindset of „many

ordinary approaches can stacked together to make an exceptional

method‟.

• Rather, based on the information available and the question that

needs answering, one or more approach may be employed, and

refined as additional data becomes available.

VI


Summary: CL prediction for glucuronidation

Finally, as the drug candidate progress through, evidence for entero-

hepatic recirculation, effects of induction and regulation of UGTs,

patient population based evaluations, etc. will be valuable.

Number

of

Compounds

Target and lead

Identification

Lead

Optimization

Candidate

Selection

Proof of

Concept

Information

on a NCE

of interest

NCE: New Chemical Entity

VI


Acknowledgements

Jennifer Bushee

Mithat Gunduz

Amanda Cirello

Bindi Sohal

James Mangold

Chitra Saran

Rutali Brahme

Project Team Members

Chemistry

Biology

Pharmacology

Metabolism and PK

DMPK

VII


4-MU kinetics: Michaelis-Menten and Atypical

Argikar UA, et al 2011. Drug

Metab. Pharmacokinet. 26(1):

102-106.

Miners JO et al 2004. Annu.

Rev. Pharmacol. Toxicol. 44: 1-

25.

Vmax (nmol/min/mg) 24

Km (µM) 210

HLM

(Link)

Hyperbolic

Hyperbolic Substrate

Inhibition

Homotropic

Cooperativity

III


Differences in incubation conditions

• Buffer – Tris-HCl vs. phosphate

• (HLM) UGT1A4, UGT1A9: up to 2x higher in Tris-HCl buffer, than

phosphate buffer

• (HLM) UGT2B7: higher activity with carbonate buffer/ Williams E

media

• HLM: higher activity with bicarbonate buffer, depending on type of

glucuronidation

• Membrane disrupting agents / latency

• Alamethicin better than surfactants

• 50ug/mg of protein (if protein concentration > 0.17mg/mL)

• Or 10ug alamethicin/mL of incubation

Latency:

Fisher MB., et al, 2000; DMD 28 (5) 560-566.

Soars MG., et al, 2003; DMD 31 (6): 762-767.

Walksy RL, et al, 2012; DMD 40 (5) 1051-1065.

Buffer:


Engtrakul JJ, et al, 2005; DMD 33 (11) 1621-1627.

Gunduz M, et al, 2010; DMD 38 (3) 361-367.

III


Differences in incubation conditions

• Organic solvents and concentrations:

• <1%

• Most UGTs (except UGT1A9, UGT2B17) are more tolerant of DMSO

than CYPs

• Saccharolactone

• Increased activity by decrease in pH, not necessarily inhibition of

beta-glucuronidases.

Solvents:

Chauret N, et al, 1998; DMD 26 (1): 1-4.

Uchaipichat V, et al, 2004; DMD 32 (4): 413-423.

Saccharolactone:

Oleson L and Court MH. 2008;

J. P. Pharmacol 60 (9): 1175-1182.


(Link) III


In vitro – In vivo Extrapolation with BSA or HSA or

IFABP: The albumin effect

• Substrates of UGTs 1A9 and 2B7 have high Km values (high uM to

low mM).

• Addition of BSA / HSA / IFABP aids IVIVE.

• Kms remain unaltered for substrates of UGTs 1A4, 1A1 and 1A6 (low

uM)

• Hypothesis: „Fatty acids are released during microsomal incubations

and out-compete the substrate.‟

Rowland A, et al, 2007; JPET 321 (1): 137-147.

Rowland A, et al, 2008, DMD 36(6): 1056-1062.

Gill KL, et al 2012 DMD 40(4): 825-835.

Which fatty acids? What are the concentrations in microsomes?

III


Fatty acids are present in microsomes:

Microsomal fatty acid concentrations

Fatty Acid Concentration (M)

RLM MLM HLM

caprylic acid - - -

myristic acid 9.0 8.2 23.1

pentadecylic acid 2.2 2.9 4.5

palmitic acid 29.3 35.4 28.4

palmitoleic acid 2.4 2.9 4.7

margaric acid <0.1 <0.1 <0.1

stearic acid 1.3 1.1 0.6

oleic acid 0.1 0.1 0.1

linoleic acid 2.3 2.8 2.2

α-linolenic acid 19.4 23.9 24.8

γ-linolenic acid 1.3 1.5 1.4

nonadecylic acid 6.3 4.2 5.3

arachidic acid 21.5 27.6 1.5

gondoic acid 1.4 0.3 0.2

trans-11-

eicosenoic acid 1.1 0.6 0.5

arachidonic acid 0.2 0.1 0.1

timnodonic acid 5.3 3.8 2.1

behenic acid 0.7 0.6 0.6

nervonic acid 0.7 0.6 0.6

-

fatty acid not observed in liver microsomal

incubation

<0.6 fold change when compared with 0 min

0.7 to 1.3 fold change when compared with

0min

>1.4 fold change when compared with 0min

• Are the concentrations

altered

• Over time

• And with or without BSA

Bushee JL, et al, 2014; Xenobiotica 44 (8): 687-695.


Fold change in microsomal fatty acids over

120min

Fatty Acid

RLM MLM HLM

Fold increase at a given time (min) in comparison with 0 min

-BSA +BSA -BSA +BSA -BSA +BSA

15 30 60 120 15 30 60 120 15 30 60 120 15 30 60 120 15 30 60 120 15 30 60 120

caprylic acid - - - -

- - - -

- - - -

stearic acid

nonadecanoic

acid

- fatty acid not observed in liver microsomal incubation


0.7 to 1.3 fold change when compared with 0min




Fold change in microsomal

fatty acids over 120min

Fatty Acid

RLM MLM HLM

Fold increase at a given time (min) in comparison with 0 min

-BSA +BSA -BSA +BSA -BSA +BSA

15 30 60 120 15 30 60 120 15 30 60 120 15 30 60 120 15 30 60 120 15 30 60 120

caprylic acid - - - - - - - - - - -

myristic acid

pentadecanoic

acid

palmitic acid

palmitoleic acid

heptadecanoic

acid

stearic acid

oleic acid

linoleic acid

α-linolenic acid 2.4

γ-linolenic acid

nonadecanoic

acid

arachidic acid 1.6

cis-11-eicosenoic

acid

trans-11-

eicosenoic acid

arachidonic acid

eicosapentaenoic

acid

docosanic acid 1.4

nervonic acid 1.4

- fatty acid not observed in liver microsomal incubation


0.7 to 1.3 fold change when compared with 0min




Microsomal Fatty acids

• Depending on the acid, concentrations remain the same or are

modulated.

• Observable species differences in liver microsomes.

• Next steps:

• Microsomes from other organs, hepatocytes, recombinant

enzymes

• Kinetics of release and inhibitory interactions

(Link) III


UGTs: Overlapping substrates (a snapshot)

Remmel RP., et al. In Handbook of Drug Metabolism. Eds: Pearson PG and Wienkers LC 2nd Edition. 2008. Informa.

(Link) III


UGT Expression: Species and Organs UGT1

Gene

Species Tissue Expression

UGT1A1 Rat, Mouse,

Human

Liver, intestine,

mammary gland

UGT1A2 Rat

UGT1A3 Human,

Mouse, Rat

Liver, intestine, testes,

prostate,

UGT1A4 Human Liver, intestine

UGT1A6 Human Liver, kidney, intestine,

brain, ovary, testes

Spleen, skin

UGT1A7 Human Gastric epithelium

esophagus

UGT1A8 Human,

Mouse

Intestine, esophagus

UGT1A9 Human Liver, kidney ovary,

testes

Spleen, skin, esophagus

UGT1A10 Human Intestine, lung

UGT2B

Gene

Species

Tissue

Expression

UGT2B1 Rat Liver, low in kidney, intestine, testes

UGT2B2,3 Rat liver

UGT2B4 Human liver

UGT2B5 Rabbit

UGT2B6 Rat

UGT2B7 Human, Rat,

Mouse

Liver, kidney, espophagus, intestine,

brain (cerebellum)

UGT2B8 Rat liver

UGT2B9 Monkey liver

UGT2B10 Human Liver, adrenals, prostate

UGT2B11 Human RNA present in liver, kidney,mammary,

prostate, skin, adipose, adrenal, and

lung

UGT2B12 Rat Liver, kidney, intestine

UGT2B13,

14, 16

Rabbit Adult liver

UGT2B15 Human Liver, prostate, testes, esophagus

UGT2B17 Human Liver, kidney, prostate, testes, uterus,

placenta, mammary, adrenals, skin

UGT2B18,

19, 20

Monkey

UGT2B21 Guinea pig

Remmel RP., et al. In Drug Metabolism in Drug Discovery and Development. Eds: Humphreys GW, et al. 2007. Wiley.

(Link) III


Current limitations of in silico approaches

• Training set based QSAR or other similar approaches

• Non-training set based approaches (molecular interaction fields

based)

• No difference between substrates, non-substrates and inhibitors

• A molecule is always assumed to be a substrate

• Binding modes cannot be separated, e.g. atypical kinetics vs.

Michealis-Menten kinetics.

• Poly-functional substrates – no differentiation between multiple

glucuronidation sites (nucleophilic functionalities)


Challenges associated with CL prediction when

UGTs play a major role

1. Demanding syntheses for reference standards

a. Formation kinetics are not always feasible

2. Phase II

a. Dependent on Phase 0 and I

b. May be rate limiting to Phase III

3. Overlapping substrate specificity

a. Lack of isoform specific inhibitors

4. Expression

a. Species differences

b. Organ differences

5. Over-expression in recombinant systems

a. HEK cells, sf/9 cells and differences in preparation of cell lysates and “Supersomes”

6. Translation from subcellular models to total CL

7. Variability in microsomes

a. Preparation

b. Incubations

8. Microsomal fatty acids and differences in membrane compositions

9. Polymorphisms


Compound 1: PK, 1mg/kg IV

10

100

1000

10000

0 3 6 9 12 15 18 21 24

Mean

Co

nc (

nM

)

Time (h)

Cynomolgus Monkeys (n = 3)

Subject Mean AUC (nM*h) 8432 + 1380

CL (mL/min/kg) 5.7 + 0.9 Vdss (L/kg) 1.7 + 0.2

T 1/2 (h) 4.1 + 0.5 MRT (0-t) (h) 4.8 + 0.8

1

10

100

1000

10000

0 5 10 15 20

Mean c

onc (

nM

) Time (h)

Wistar Han Rats (n = 2)

Subject Mean AUC (nM*h) 4664

CL (mL/min/kg) 9 Vdss (L/kg) 1.6

T 1/2 (h) 2 MRT (0-t) (h) 2.2


Chemical entity based approach is an alternative

depending on the phys-chem nature of compound of

interest

Lombardo FL, et al 2012; J. Clin. Pharm. 53(2) 178–191.

Percent <2-Fold Values for Each Prediction Method for Human CL Clustered by

Charge Type


Drug-Drug Interactions involving UGTs

Remmel RP et al. 2008. In Drug-Drug Interactions. Ed:

Rodrigues AD. Informa.


Mechanistic rationale for DDIs with UGTs and

glucuronides

1. Direct enzyme inhibition by competition

2. Induction of individual UGTs

3. Depletion of UDPGA

4. Inhibition of transport of UDPGA

5. Futile cycling: Inhibition of renal elimination of glucuronides

6. Interruption of enterohepatic recirculation/inhibition of intestinal

microflora.

7. Alteration of membrane transport of glucuronides

Remmel RP et al. 2008. In Drug-Drug Interactions. Ed: Rodrigues AD. Informa.


Assessing Drug-Drug Interactions: challenges

• Lack of specific probe substrates

• Determination of fm,UGT is difficult due to

• Entero-hepatic recirculation

• Glucuronide in urine/bile is not representative of the above

• Biliary elimination of glucuronides: Invasive

• Differences in extrahepatic glucuronidation: preclinical to human

• UGTs 1A3, 1A9, 2B7: kidney

• UGTs 1A1, 1A8, 1A10: intestinal tract (differences between duodenum, ileum, jejunum)

• Accurate prediction requires use of appropriate scaling factors – relative organ weights, microsomal yields, differences between activities and yields

• Extrahepatic UDPGA (20-100uM) is lower than liver (~400uM).

Capiella M. 1991 J Clin. Pharm. 41: 345-350. Goon D and Klassen CD. 1992, Tox. Appl. Pharmacol. 115: 253-260.

Zhou J and Miners J. In Enzyme Kinetics in Drug Metabolism: Fundamentals and Applications. Eds: Nagar, S, et al..

Springer/Humana Press


Assessing Drug-Drug Interactions: practical

limitations

• DDI predictions are off due high Kms.

• Hence, addition of BSA: Fluconazole-zidovudine, valproic acid-

lamotrigine

• Without BSA: fatty acids outcompete the perpetrator

• With BSA: victim/perpetrator bind to albumin

Remmel RP et al. 2008. In Drug-Drug Interactions. Ed: Rodrigues AD. Informa.

Zhou J and Miners J. In Enzyme Kinetics in Drug Metabolism: Fundamentals and Applications. Eds: Nagar, S, et al..

Springer/Humana Press


Assessing Drug-Drug Interactions: practical

considerations and unknowns

Practical Considerations

• Consideration of hepatic inlet concentrations

• Consideration of therapeutic concentrations

• Use of rUGTs

• Understanding mechanism of clearance, fm and fm,UGT

• Consideration of system dependent kinetics – rUGTs vs. HLMs

Unknowns

• Dimerization: Effects of homo-dimerization, hetero-dimerization.

• Microenvironment: dependence on membrane integrity, accessory

proteins, redox states, cellular environment.

Challenges and Opportunities with UGT Enzymes · • In vitro scaling methods • Simple allometric...

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