The document discusses optimizing ADME and PK properties in drug development. It addresses common mistakes such as believing that intrinsic clearance cannot be optimized or that increasing plasma protein binding will always benefit PK. It emphasizes that intrinsic clearance, uptake clearance, and renal clearance all contribute to in vivo clearance. Good quality experimental data is important for accurate prediction of human PK. Formulation strategies can improve bioavailability when absorption is limited, but not if clearance is the dominant elimination pathway. The effects of plasma protein binding on free drug exposure are also explained.

1. Optimising ADME and PK properties:
Common mistakes made and how to identify and
resolve the key issues
XenoGesis Limited | BioCity Nottingham, Pennyfoot Street, Nottingham, UK, NG1 1GF | richard.weaver@xenogesis.com | www.xenogesis.com

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XenoGesis - quick introduction
• The services offered:
• Experimental in vitro and in vivo DMPK/ADME studies, bioanalysis and in vitro pharmacology
• Pre-clinical PK/PD modelling, interpretation and human PK and dose prediction
• 8 pre-clinical client candidates advanced to clinic
• Consultancy delivering expert drug research & development advice
• The Client base:
• UK, Europe, US, Singapore, Australia
• >200 companies in 8.5 years - SMEs, mid-sized Pharma. 25 Universities
• High % repeat business (74% of all quotes issued)
• The Company:
• Founded in November 2011 at BioCity, Nottingham, with 3 staff
• 95% privately owned
• High year on year growth (30% growth and 50% overseas revenue 2018/19 YE)
• The Team:
• Richard Weaver Ph.D., FRSC – CEO and Founder
• 32 further members of staff
• Highly experienced scientific staff, with Pharmaceutical Industry or CRO backgrounds

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Contents
• Intrinsic clearance
• IVIVE
• When IVIVE won’t work
• Data quality
• Why this isn’t always the case
• How things can remarkably make sense when the data is good!
• Plasma protein binding
• The myths that still keep being believed
• What you need to know
• Increasing bioavailability
• How you can’t necessarily formulate your way out of it, but many think you can
• But formulation can help increase bioavailability dramatically

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Intrinsic clearance
What’s the point in optimising in vitro CLint?
Statements you might hear:
• “We have compounds with high in vitro CLint but low in vivo clearance”
• “I have compounds with high oral bioavailability despite high CLint”
• “We spent 6 months optimizing our compounds to have low CLint but in vivo clearance is high”
• “When I plot intrinsic clearance in vitro versus in vivo clearance there is no correlation”
• “We use microsomes as hepatocytes are too expensive”
• “I’ve read literature that says hepatocytes are poorly predictive of clearance”
• “I’m measuring in vivo PK anyway, why should I worry about the in vitro data?”
• “It’s cheaper just to do the PK in the far east”

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Intrinsic clearance
• Client has plotted Clint vs. in vivo CL
• Poor correlation
• Option 1:
• “Hepatocytes don’t predict clearance, let’s stop using this assay”
• Option 2:
• Science
• We always recommend Option 2
• Let’s try and understand what’s going on….
CLint
Observed
Cl

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Intrinsic clearance and in vivo clearance
• Intrinsic clearance (CLint)
• The volume of incubation media from which all drug is removed per unit time
corrected for the number of cells
• Units are typically µL/min/106cells
• Closed system
• No flow, only binding is to cells (or plastic)
• In vivo clearance
• Volume of plasma (or blood) from which all drug is removed per unit time
corrected for bodyweight
• Units are typically mL/min/kg
• Occurs as unbound drug passes through eliminating organ (e.g. the liver)
• Plasma protein binding and liver blood flow are important

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Predicting in vivo clearance (IVIVE)
• Apply well-stirred model to the in vitro Clint dataset from before
• Important to convert everything to “unbound” values
• Correlation now looks better, but there are some outliers with
significant under-prediction… why?
Predicted Cl
Observed Cl

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Why can we get under-prediction of in vivo clearance
• Standard hepatocyte incubations measure loss of compound due to
metabolism
• While this is the most common route of elimination for basic/neutral
permeable compounds, for acids and/or low permeability compounds
hepatic uptake and/or renal clearance will often dominate
• Observed clearance is sum of all clearance pathways
• CL,total = CL,hepatic metabolic + CL,hepatic uptake + CL,renal (+CL,other)

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Hepatic uptake
• Acidic containing compounds are often
substrates for hepatic uptake (e.g. statins)
• A standard hepatocyte incubation cannot
measure this. As both cells and media are
sampled, drug taken up into the cells isn’t “lost”.
• By sampling the media alone in parallel with the
cells+media we can measure uptake
• Example shows Atorvastatin
• Level of drug in media drops dramatically at early
time-points as compound is rapidly taken up into
cells
• Concentration in cells + media decrease much
more slowly reflecting metabolism CLint being <<
uptake CLint

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Data quality
“I’ve used the WSM, corrected for renal clearance
and uptake but I still get a poor correlation”
• We often review 3rd party DMPK and physicochemical data
• Sometimes the data is fine, sometimes it is not
• Is your CLint data correct?
• We routinely find our CLint values are higher than third party/literature data
• Our assay has been carefully optimised to give the maximal CLint and gives
better correlations with in vivo data than literature values (see right)
• Is your PK data correct?
• Ion suppression (e.g. as a result of PEG in vehicles or leached from plastics) can
result in under-estimates of plasma concentrations and over-estimate of
clearance
• Enterohepatic recirculation can lead to an apparent lower Cl and higher Vss

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Data quality – an example
• 3rd party data
• Hepatocyte CLint 7µL/min/106cells, Fu,p = 0.57
• CLp = 142mL/min/kg
• XenoGesis data
• Hepatocyte CLint =123µL/min/106 cells, Fu,p = 0.39
• CLp = 46mL/min/kg
CLp
(mL/min/kg) 3rd Party XenoGesis
Predicted 22 40
Observed 142 46
• XenoGesis hepatocyte CLint 18x higher and CLp 1/3 of 3rd party data
• Predicted clearance is within 1.2 fold of the observed using XenoGesis data
versus 6.5 fold for the original data
• PBPK modelling captures the observed IV profile using XenoGesis data

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Plasma protein binding
• Free drug hypothesis
• “In the absence of active transport, the free drug concentration in non-eliminating tissues is
the same at steady state”
• Misconception of PPB
• “We need to optimise PPB”
• “Increasing PPB will give us better PK”
• “Increasing PPB will increase my half-life”
• “Lower PPB = greater free drug exposure”
• This will be true if the total drug exposure is the same but…
• Plasma protein binding does not affect free drug exposure
• We should optimise free drug exposure not free fraction
NRRD, 2010, p929

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Plasma protein binding
• Yes, you should!
• PPB is essential to translate in vitro to in vivo data
• CLint to CL
• Total in vivo exposure to in vitro pharmacology
• ….and to compare across species
• Unbound exposure will drive toxicology as well as positive pharmacology
• If human is 10x more free than the tox species, you need to know about it
• Allometric scaling should correct for species difference in binding
• Measure dog PPB in same individuals as PK
• Differences can be significant
• It’s important to know what PPB is, but it shouldn’t (can’t be!) “optimised”
Wait… So… I shouldn’t measure PPB?

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Metabolism and absorption both contribute to bioavailability
Formulation strategies will generally
only improve bioavailability if this is
absorption limited

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Case study – Enhancement of oral bioavailability for NCE
• Background:
• Requirement: To provide a formulation to enhance exposure for use in toxicology studies
• Very poor aqueous solubility (< 1 µg/mL at pH 7.4), moderate permeability (Caco-2 A-B: 6 x 10-6 cm/s)
• Limited exposure observed in animal studies (plateau at 20 mg/kg dosing).
• Clearance was < 5% liver blood flow – if all compound is absorbed, bioavailability > 95% should be
possible
• Fraction absorbed and escaping GI metabolism (Fa) is limiting bioavailability:
• Data suggests poor solubility is limiting exposure due to poor absorption

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Formulation strategy
PK screening of
formulations
In vivo
(rat/dog)
8-12 weeks
Optimal formulation strategy
based on PK data
Drug substance nano-milling
Cyclodextrin complexation
Lipidic/self-emulsifying system
Amorphous solid dispersion
(spray-drying or HME options)
Test compound very poorly soluble in water, moderate permeability
Stage 1: Drug substance review
Physicochemical and biological (DMPK) characterisation, anticipated dose in humans (or dose range in animals)
- gap analysis and gap filling
Stage 2: Enabling technologies rapid screen
Rapid assessment of solubility-enhancing formulation technologies
5 g drug substance for full screen

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Success !
• Significantly improved exposure for both spray-dried formations at 500mg/kg
• Bioavailability > 80% for both formulations & dose levels versus < 2% for simple API suspension
• AUC and Cmax generally increased with dose from 50 to 500mg/kg
Outcome consistent with bioavailability being limited by solubility &
poor absorption rather than clearance

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Show-casing spray-dried dispersion technology
• Raloxifene used to showcase spray-dried dispersion (SDD’s) technology
• The use of SDD's to enhance the delivery of BCS Class II/IV molecules is now a recognised
formulation approach, that can be taken to the clinic
• Raloxifene administered orally in both crystalline form and as an SDD

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How DMPK (people) should influence in Discovery and beyond
• Identify the key DMPK issues early and fix them
• Help select the best compounds (or design better ones)
• Help fix bioavailability issues
• Increasing exposure for PD or Toxicology studies or in human
• Extended release to “rescue” poor T½
• Predict human PK and dose early and refine as project progresses
• Continue to refine as Phase 1 data becomes available
• Will guide API scale-up demand/cost of goods
• Prepare your asset for due diligence with large pharma/partnering
• Assess human DDI risk (CYPs, transporters)
Target
Selection
Hit
Identification
(HI)
Lead
Identification
(LI)
Lead
Optimisation
Candidate
Drug
Nomination
Pre-Clinical
Development
Phase 1 Phase 2a Phase 2b
Phase 3 &
Launch

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Thank you
Please contact us to learn more:
info@xenogesis.com
richard.weaver@xenogesis.com
rachel.hemsley@xenogesis.com
visit our Website or follow us:
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