Modelling for drug development : A Medicines For Malaria perspective

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Sam Jones, PhD

Manager, PKPD modelling, MMV

08/10/2024

Medicines for Malaria Venture : Who we are

MMV is a product development partnership, Swiss foundation and US charity of over 100 people working towards one mission:

to reduce the burden of malaria in disease-endemic countries by

discovering, developing and facilitating the delivery

Of new, effective and affordable �antimalarial drugs

https://www.mmv.org/newsroom/news-resources-search/annual-report-2023

(Aussi en français)

● 2

Clinicals (CRO or trial sites)

43%

A network of over 700 R&D and access partners from public and private sectors*

Service providers

18%

Academia

20%

* 2014-2023, across drug discovery, development and access activities (excludes consultants, Corporate Affairs, Finance, IT and HR).

partners in

>700

72

Countries

● 3

MMV-supported projects

MMV support to projects may include financial, in-kind, and advisory activities.

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Footnotes: Included in MMV portfolio after product approval and/or development. DNDi and partners completed development and registration of ASMQ and ASAQ. │ Global Fund Expert Review Panel (ERP) reviewed product – permitted for time-limited procurement, while regulatory/WHO prequalification review is ongoing │ Pediatric formulation.│ Via a bioequivalence study. Past partners are in brackets (-).

Candidate�profiling

Lead optimization

Research

A.K.A Drug Discovery

Upcoming MMV Tools Webinar to AMMNET will explain our free to use tool MMVSola

Modelling human pharmacokinetics and dose is the key driver for optimization of all discovery projects

 We are also starting to actively model all our data and use this with generative design to harness ML for malaria drug discovery

Time

Drug concentration

Blood plasma (and closely related tissue)

Other tissues

Pre-clinical

Human

volunteers

Malaria

patients

Clearance of drug from blood by biological processes (liver, kidneys)

Absorption

Exchange of compound between blood and tissues

Pharmacokinetics (PK): What the body does to the drug

Inter-individual variability (IIV)

Ac

Ap1

Dose

*

Fabs

Q1

CL

Volume of distribution : a theoretical value that describes how a drug is distributed throughout the body relative to the concentration of the drug in the blood or plasma.

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High : the drug spreads widely into body tissues such as fat and muscle

Low : the drug mostly stays in the blood or plasma

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Pharmacokinetics (PK): What the body does to the drug

Compartmental model can be expressed as series of ordinary differential equations

Time

Drug concentration

Pharmacokinetics (PK): What the body does to the drug

Fit the model to the data to obtain parameter estimates

Estimate parameters using non-linear mixed effects modelling

CL = 2.3 L/hour

Time

Parasite number

s

Drug Concentration

PK/PD is the relationship between pharmacokinetics pharmacodynamics

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We want to model this relationship

(which describes the kill rate)

Pharmacodynamics (PD): What the drug does to the parasite

Kill Rate

Drug Concentration

Kill Rate

Drug Concentration

PK/PD: Combined PK and PD models

E_max killing model (others are available)

E_max

EC₅₀

hill

Parameters can be estimated by fitting structural model (the ODEs) to the data, using a non-linear mixed effects estimator

Such as Monolix (others exist);

https://lixoft.com/products/monolix/

IIV : Inter-individual variability

RSE% : Residual standard error (a measure of uncertainty)

Some parameters may be affected by covariates

such as bodyweight (allometric scaling)

Parameter estimation

Model parameters (values, IIV, RSE and covariates)

Structural model

+

• We’ve built a mathematical description of the system
• Adjusting specific values allows us to make predictions

Time

Drug concentration

Test a range of untested doses

Adjust the value of DOSE in the system

Compare resistant v sensitive parasites

Adjust PD parameters

(likely EC50, maybe EMAX)

Time

Parasite number

• Other examples:
• In Children
• In Pregnant women
• Differences in PK when fed/fasted

100mg

80mg

40mg

20mg

Resistant

Sensitive

Making predictions

In vitro assays

Pre-clinical species

Healthy human volunteers

Malaria patients*

*Or those at risk of infection

Higher doses

In children

Pregnant women

Take decisions, design next experiment or clinical trial

Product development

Patient confirmatory

Preclinical

Human volunteers

Patient exploratory

Early Development

Data

Model

Next step

Predict

Development of

combinations

IIV and RSE reduce

Some IIV explained by covariates

IIV and RSE reduce

Better estimation of drug-drug interactions

First parameterization of combination

Sensitivity analysis of drug-drug interaction

In vitro assays

Animal experiments

First in Human (PK)

Volunteer infection (PK + PD)

In vitro combination checkerboard

Phase 2 trials +

Interaction studies

Phase 3 non-inferiority trial against standard of care

Decide doses to be tested in First in Human and Volunteer Infection Studies

Decide dose ratio and doses to be tested in patients

• Adjust doses in special population if needed (eg children)
• Probability of success in phase 3 trial (e.g. non inferiority to Coartem)

Predict the probability of success once on the market �(probability to reach the WHO target efficacy)

Monotherapy PK+PD

In silico combination

In vivo combination PK+PD

We use in silico proxies of clinical endpoints from WHO trial guidelines (we assume patients don’t have fever, so these endpoints are “optimistic”)

Intervals reflect IIV and uncertainty on both PK and PD parameters

Simulate patients at a range of doses (PK). What happens to the parasites (PD), after a range of doses?

Efficacy : Uncomplicated Malaria

Target: 95% APR28 (but this slide is monotherapy…)

APR28 = Adequate Parasitological Response at 28 days

Individual

Population

Efficacy : Uncomplicated Malaria

Target: 95% APR28 (this slide is combination!)

• Take two monotherapy models and combine them for an in silico combination model

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• Perform “isobole simulations” where the APR28% of the combination is tested at many combinations of doses of both compounds

• This line reflects the median dose (combination) at which 95% APR28 is achieved

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• The grey band reflects the IIV and uncertainty

Time

Parasite number

s

Drug Concentration

Preventive Efficacy : Chemoprevention

MIC

Target in early development : X time above MIC

• MIC : Minimum Inhibitory Concentration

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• Lowest concentration of compound at which growth of parasites is inhibited

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• Can be calculated from in vitro and animal experiments, then updated when in vivo data is available

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• Aim for 1-month time above MIC for oral chemoprevention

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• Aim for 3- or 6-month time above MIC for long-acting injectables

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x

Preventive Efficacy : Chemoprevention

Target: 80% Preventive Efficacy

Simulate drug concentrations (PK) and introduce infectious bites into the system (with PD parameters)

MIC

Time [days]

Placebo

Intervention

Incidence rate in either arm allows calculation of preventive efficacy from the incidence reduction ratio

When phase 2 data is available, a time-to-event model can be built from the trial data

Prior to phase 2, we use PK/PD parameters of the combination with simulated infectious bites to generate in silico incidence profiles

Final thoughts

https://www.mmv.org/

https://pmxafrica.org/