2022 Speakers and symposia information to follow in due course

Keynote Speakers

Trevor Mundel  – Health Equity:  One Simulation at a Time

Cheaper data processing capacity and a growing number of open source tools are creating an opportunity for Africa to adopt revolutionary new approaches to measuring the safety and efficacy of essential health interventions in African populations. Dr. Mundel will focus on four areas where African researchers are creating opportunities for the continent to leapfrog to more affordable and accessible pharmacometric technologies, including:

  • Pharmacogenetics via the work of Human Heredity and Health in Africa (H3Africa) and the integrated drug discovery work of H3D;
  • Clinical trial simulations to identify the most predictive indicators for evaluating the safety and efficacy of new therapeutics and new tools to address regulatory capacity gaps;
  • Pan-African pharmacovigilance to ensure the safe scaling of new interventions to a broad population and to inform future product development priorities; and
  • A comprehensive learning system that integrates data on diagnosis, delivery, and outcomes into a highly powered platform capable of driving health system innovation.  

Dr. Mundel will also explore how partners across governments, philanthropies, and the private sector can work together to strengthen the science of pharmacometrics in Africa.


Jeanine Condo – The use of mass campaign to fight against NCDs in LMICs: case of Rwanda

For the past decades, non-communicable diseases (NCDs) have been neglected despite its contribution to cause of death in Africa. Nearly half of the population in this region already suffer from hypertension, a well-established precursor to NCDs such as heart attacks and strokes.  NCDs have become the principal cause of morbidity, mortality and disability affecting the quality of life of patients suffering from NCDs. Unfortunately, patients with NCDs present themselves at a late-stage of clinical symptoms after impairment of key organs.

Access to a comprehensive quality of care is not only expensive but also unavailable in countries with limited resources. Rwanda has employed unconventional methods such as the healthy mass campaign “Car Free Day “ that simultaneously fights NCDs and air pollution.

Prof Jeanine Condo will use this program to highlight practical lessons learnt from  collaborative efforts to address Africa’s disease burden.


Lena Friberg –  Less is more? The tale of a myelosuppression model

One of the most cited pharmacokinetic-pharmacodynamic models in the literature is the “semi-physiological model of myelosuppression”, originally developed to describe the time-course of total white blood cells after chemotherapy treatment. The model was established with the idea of being general and parsimonious, with few parameters, rather than a ‘perfect’ model for one particular dataset. Nearly 20 years later, it is still a common building block to describe changes in blood cell counts over time.

 The presentation will cover

  • the story of the emergence of the model
  • applications and ’add-ons’ – other blood cells, rescue treatment, other disease areas
  • its use in drug development
  • potential value for dose individualization
  • connections to other PD-models and
  • ‘tips and tricks’, including common misconceptions

Dr Friberg will also discuss the integration of the model in ‘frameworks’ that integrates various models for other adverse effects, tumor size, febrile neutropenia and survival of value for exploring dosages.


Rada Savic – Development of Computational and Translational Tools for Drug Development of Combinational Regimens for Treatment of Tuberculosis

Recent TB drug discovery and development successes have delivered a number of new drug approvals, late stage drug candidates and repurposed antibiotics (https://www.newtbdrugs.org/pipeline/clinical), leaving the TB community with a good problem to have: with too many possible combinations, how do we prioritize new regimens for testing in resource and cost intensive clinical trials?

Given the complexity of human TB disease pathology and pathogen physiology, whether regimen ranking in any preclinical model systematically and quantitatively holds true in patients is often questioned. Indeed, predicting the clinical potential of regimens that are made of 3 or 4 antibiotics is challenging for several reasons. First, different bacterial populations are differentially susceptible to each antibiotic, and the relative representation of these populations is hardly recapitulated in mouse models. Second, the kinetics of drug penetration at the sites of disease is affected by lesion structure, which again differs between mice and men. Third, drug pharmacokinetics and drug-drug interactions are species specific, and human-equivalent doses of preclinical development candidates cannot be accurately achieved in the mouse. Lastly, the definitions of clinical endpoints and biomarkers of drug response are distinct in preclinical and clinical studies, which poses another challenge for accurate translation. Each of these challenges represents a potential obstacle for successful regimen development, and none of them exists in silo; they are all inter-related.  Rather than insufficient data, the largest gap for successful regimen development is knowledge integration across all of these challenges and a deep understanding of how they inter-relate and quantitatively translate into the common goal, which is a relapse-free cure for all patients following short course treatment.

To that end, we integrated vast of drug development data ranging from preclinical studies in mice and rabbit, and clinical (Pharmacokinetic, Pharmacodynamic, biomarker and clinical outcome) data from Phase 2A, 2B and phase 3 trials including >10 distinct drugs. Based on this database, we develop set of translational and predictive models/tools that can ensure successful drug development and selection of the best combinations. The tools include empirical machine learning algorithms, mechanistic PKPD models, systems pharmacology/immunology model as well as integrative biomarker/clinical outcome relationships.  Next, we propose novel early and late stage clinical trial designs and adaptive platform that can be used for drug regimen optimization linked to an in silico clinical trial simulation tool.

Scientific Symposia

Clinical disease-drug interactions: model-based approaches for tailoring treatment in patients at risk

Chairs: Johan Wallin, Holly Kimko

This session will cover concepts of interactions between disease and drug disposition. Patient disease status, such as nutrition status, inflammation, renal and hepatic impairment or tumor activity, may influence the pharmacokinetics of drug on several levels from absorption to elimination. Knowledge about the interplay between disease and pharmacokinetics may be key not only for dose individualization, but also for accurate capture of exposure-response in clinical drug development.


    1. Phumla Sinxadi – Clinical pharmacology overview of drug-disease interactions
      The talk will introduce the clinical pharmacology concepts that link medical practice and laboratory and data sciences. The importance of dose selection and adjustments by medical practitioners to maximize drug effects and minimize side effects will be covered using examples from different therapeutic areas; including oncology, immunology, infectious and cardiovascular diseases.
    2. Maria M. Posada- Model-based approaches for predicting drug-disease interactions in special populations
      In this presentation the speaker will discuss the challenges and progress in the prediction of the effect of hepatic impairment on drug exposure.
    3. Paul Baverel- Time-varying clearance and disease dynamics in oncology: the effect of disease on biotherapeutic PK
      A population PK analysis of durvalumab (an anti-PD-L1 monoclonal antibody approved for bladder cancer and NSCLC) will be presented to understand the association of disease status at baseline and on-treatment with biotherapeutic clearance and to explain an increasing trend of exposure over time observed among patients who benefit from therapy. Possible mechanism of actions will be evaluated by linking biomarker longitudinal data with durvalumab serum levels and derive a semi-mechanistic PK model. Finally, the impact of the time-varying PK and association with disease status will be discussed in the broader context of exposure-response and dose justification.
    4. Elisabet Nielsen – Pathophysiology and alterations in PK/PD of antibacterial agents in critically ill patients
      Sepsis remains a major cause of morbidity and mortality in critically ill patients, and prompt antibiotic therapy is critical to increase survival. However, choosing an appropriate antibiotic regimen is challenging given the complex and dynamic pathophysiology of sepsis. Sepsis is a result of a dysregulated host response to infection, which manifests as vasodilation, increased capillary permeability, decreased systemic vascular resistance, and low blood pressure. This leads to alterations in organ blood flow and membrane permeability that, together with applied therapeutic interventions, may have a profound effect on drug pharmacokinetics.

Pharmacometrics and QSP: Convergence, Commonalities, and Continuities

Chairs: Matthew Riggs, Stacey Tannenbaum

Although pharmacometrics (PMx) and quantitative systems pharmacology (QSP) have seemingly emerged independently, they have converged to share many methodologies, strategies, and goals, and have both contributed greatly to quantitative pharmacology efforts promoting model-informed drug development (MIDD).  This session will serve to examine the interphase of PMx and QSP approaches, and to clarify the varied terminology around mechanistic modeling. Case examples will depict the continuum of moving from simple to complex models based on the questions at hand, while adding to our understanding of commonalities between PMx and QSP that continue improving MIDD decision support.


    1. Matthew Riggs and Stacey Tannenbaum – Compare, Contrast, and Converge: PMx and QSP within a MIDD decision support framework.
      The joint presentation will review the following areas related to PMx and QSP: the types of research and development questions they inform, the teams/partners that are interfaced to ensure successful integration with MIDD, the types of models, methodologies, data and software that are often used, and considerations for establishing model credibility (e.g., validation, verification and uncertainty qualifications) in context with the question(s). The topics above will be described in general terms. Similarities, as well as separate approaches, between PMx and QSP will be used to highlight convergence areas of these disciplines and to show integration points that will facilitate the advancement of MIDD.
    2. Rene Bruno – The evolution of M&S in oncology — from empiric to mechanistic
      Modelling tumor data dates back to the 1930’s; the bulk of tumor modelling began in the 1960’s and has broadened considerably this century. The development and history of these efforts will be reviewed: beginning with simpler equations and moving to more recent applications describing cell signalling, biomarkers, microenvironment, apoptosis, and evolutionary resistance. These approaches are converging toward expanded mechanistic and SP models. Following this review to consider of what can be done, we’ll explore considerations of what should be done, i.e. – how complex or how simple should the model be to appropriately inform the development questions (MID3) at hand.
    3. Mélanie Wilbaux – Contribution of machine learning to clinical tumor growth inhibition modeling
      Machine learning opens new perspectives in identifying predictive factors of efficacy in oncology Phase I studies and can contribute to improve PKPD models predictions. In this work, clinical efficacy of FGF401, evaluated in a Phase I/II study in hepatocellular carcinoma, was described with a tumor growth inhibition model. Machine learning was used to derive a composite score of baseline factors predictive of time to progression. The two approaches were combined by including the score as a covariate on the resistance parameter, leading to 30% reduction of its variability. The final model was used to simulate patients’ characteristics effect on efficacy.
    4. Shruti D. Shah – Logical modelling of tumor-immune cross-talk network to predict the response to therapy for muscle invasive bladder cancer (MIBC)
      The tumor-immune system relationship is extremely dynamic. Here we use Boolean modelling approaches to capture the dynamic changes of the immune microenvironment in bladder cancer with treatment. We have constructed a knowledge-based Bladder-Macrophage-CD4 T-cell (BMC) network, which allows us to study the temporal pattern of the dynamic changes of the tumor immune microenvironment in response to treatment. The BMC Boolean model can be used to gain mechanistic insight into the tumor-immune cross-talk in bladder cancer under different treatment conditions and can be used to generate hypothesis for designing novel combinations that can sustain pro-inflammatory phenotype.

Training of the next generation of pharmacometric talents around the world: Competencies – Barriers – Opportunities

Chairs: Stefanie Hennig, Charlotte Kloft, Robin Michelet

Good pharmacometrics education is key to foster the further development of the field and allow implementation of model-based approaches with high quality in a way that directly and indirectly affects patients. This symposium aims to provide an overview of different aspects, which aid or hamper this education globally. Furthermore, capitalizing on the variety of approaches, useful strategies to bring out the highest quality in students are investigated and ways forward will be presented. Common factors between successful programs such as program size, industry sponsorship, student level, and future prospects will be identified and their relative impact assessed, ultimately increasing pharmacometric education and collaboration on a national and international level.


    1. Charlotte Kloft – A German perspective: What does “interdisciplinary” in training new pharmacometric talents really mean? Joint efforts and experiences from PharMetrX and beyond
      The Graduate Research Training Program PharMetrX: Pharmacometrics & Computational Disease Modelling is a joint initiative in Germany between Freie Universität Berlin and Universität Potsdam, bridging Pharmacy and Mathematics, and supported by a consortium of the research-driven pharmaceutical companies. It is a truly trans-disciplinary research program, supervised by leading experts in their fields, supported by a specifically tailored training module program and embedded into a network of peers. In this presentation, the program will be introduced after which lessons learnt, pitfalls, challenges and opportunities of an international and interdisciplinary training program such as this one are highlighted.
    2. Jens Markus Borghardt – An industry perspective – Benefits of an industry mentor for PhD students
      PK/PD modelling and simulation (/pharmacometric) groups exist in diverse areas of the pharmaceutical industry, ranging from early drug discovery to clinical development. These different phases offer unique insights into both PK and PK/PD. In common for all modelling activities in industry is that the value of modelling is mostly defined by the impact on drug development programs. This work allows a unique view on pharmacometric analyses and on how to optimally apply these. Providing and supporting PhD students with an industry perspective and explaining impactful questions early on allows to optimally prepare the next generation of pharmacometricians for the future.
    3. Colin Pillai and Paolo Denti – Building Pharmacometrics capabilities in Africa: history, methods, outputs, lessons learnt
      Since 2009, a small number of industry and academic pharmacometricians, have collaborated with multiple institutions across Africa to build pharmacometrics capacity. In 2018, we expanded our reach by establishing Pharmacometrics Africa (https://www.pmxafrica.org/), a collaborative platform for open-access quantitative clinical pharmacology educational programs. Our tactics includes virtual trainings (short webinars and longer online courses), workshops (3-5 day hands-on and shorter advocacy events) and gradual incorporation into degree programs. While these investments have helped advance the understanding of dose-exposure-response relationships for several diseases of global health relevance, much remains to be done. This talk will outline progress, challenges and lessons learnt.
    4. Guangda Ma – A remote student’s perspective: Competencies – Barriers – Opportunities when training within a small and remote group
      New Zealand is a small country of 4 million located in the South Pacific Ocean. Despite our remoteness, New Zealanders have played a crucial role in the advancement of pharmacometrics (PMX). Training and learning within a small PMX group can sometimes be challenging, though this is usually fulfilling and rewarding. We are fortunate in New Zealand to have local opportunities to learn from, and connect with other PMX groups such as Otago, as well as local conferences such as PAGANZ. This presentation aims to provide an overview of my experiences training to be a clinical pharmacometrician in New Zealand.
    5. Bernd Meibohm and Stephan Schmidt – A US perspective: How to train the next generation of pharmacometricians by engaging all stakeholders?
      Graduate and post graduate training in pharmacometrics in North America is challenged by a decline in academic training sites and mentors, but a step increase in the need for pharmacometricians and pharmacometrically-competent preclinical and clinical pharmacologists in the pharmaceutical industry, research organizations and regulatory agencies. At the same time, new therapeutic modalities such as advanced protein scaffolds, nucleic acid derivatives, gene therapy, and cell-based therapeutics drastically expand the knowledgebase and toolset needs for pharmacometricians to be successful in the drug development environment of the third decade of the 21st century and beyond.
    6. Teresa Dalla Costa – A Latin American perspective: Building Pharmacometrics capabilities in Latin America: Competencies, Barriers, Opportunities and the Future
      Pharmacometrics is an incipient science in Latin America, still limited to the academic sector. Since 2017 regional research groups gathered in RedIF to foster pharmacometrics through the promotion of on-ground educational activities and scientific meetings. Pharmaceutical Industry is slowly recognizing the importance of M&S for generics development, and regulatory agencies are faced with the challenge of training their staff to handle drug registration processes based on these approaches. Effort in training new pharmacometricians by local groups should be boosted by the generation of regional job opportunities to avoid skill drain and support the growth of local community.
    7. Panel discussion with all speakers of this symposium – A Worldwide perspective: How to train the next generation of pharmacometric talents?
      After insights from all over the world, it is time to put all the ideas together in a panel discussion. In this session, we will identify common challenges raised and intent to formulate possible solutions both using input from the speakers and the audience. Furthermore, the potential of connecting global partners to tackle these challenges and increase opportunities for young scientists will be elucidated, ultimately allowing for improved pharmacometric education and collaboration on a national and international level.

Impact of Pharmacometrics on Global Policy: Application to Global Health

Chairs: Elin Svensson, Christine Sekaggya 

The science of pharmacometrics is most often applied to drug development programs of novel compounds and used to support registration with regulatory bodies. However, its application to guide global policy recommendations by organizations such as the World Health Organization (WHO) is seldom discussed. This session aims to present the use of model-based analysis to help guide dosing and treatment recommendations by WHO, focusing on the experiences of currently available medicines of high importance for global health. Examples for big killers as malaria and tuberculosis will be showcased, as well as applications for tropical neglected diseases.


    1. Thomas Dorlo – Modelling and simulation to guide treatment of the neglected tropical disease visceral leishmaniasis
      Visceral leishmaniasis is currently the second largest parasitic killer, after malaria. Geographical differences in efficacy are apparent for almost all of the available drugs for this neglected tropical disease. This talk will highlight the contribution of pharmacometric modelling approaches to further optimize available therapies and guide dosing recommendations for this neglected disease in Eastern Africa, through collaborations with the product development partnership Drugs for Neglected Diseases initiative (DNDi).
    2. Joel Tarning – Pharmacometric modelling of antimalarial drugs to inform dosing guidelines
      Young children (<5 years of age) are especially vulnerable to malaria and approximately 61% of all malaria deaths worldwide occur in this population. Oral artemisinin-based combination therapy and parenteral artesunate is the recommended first-line therapy for uncomplicated and severe malaria, respectively. Clinical trial data and pharmacometric modelling was used to evaluate antimalarial drug exposures in children with uncomplicated and severe malaria. We demonstrated that children receiving standard dosing achieved lower drug exposure compared to adults. This could lead to treatment failures and development of antimalarial drug-resistance. Modelling and simulation were used to propose novel optimised dose regimens for these children.
    3. Kelly Dooley – Treatment of Tuberculosis: Advancing Towards a Better Future
      This talk will describe how translational and clinical pharmacology are being used to advance treatment and prevention strategies for tuberculosis, now the #1 infectious disease killer of humans on the planet
    4. Anneke C. Hesseling – Use of pharmacometrics to study and guide novel tuberculosis treatment strategies in children
      About 1 million children fall ill with tuberculosis (TB) each year. Professor Hesseling will speak about how pharmacometrics has helped in designing pediatric trials of novel TB treatments and how the results therefrom are used in forming WHO guidance on better TB treatment for children.

Methodology: Model Averaging and Estimands

Chairs: France Mentré

Model-based and/or pharmacometrics analyses of pivotal trials is not fully accepted by statisticians and regulators. The two topics of this methodology session, model averaging and estimands, should contribute to bridge the gap between statistics and pharmacometrics. Model averaging is one solution to the problem raised by model selection leading to too optimistic results for statistical inferences and/or tests. Model Averaging at the design stage is also promising. The estimands framework shifts the attention from the HOW to the WHAT in the design and analysis of confirmatory trials. It is an attempt to restore “the intellectual primacy to the questions we ask, not the methods which we answer them” Sheiner (1991).


    1.  Andrew C. Hooker – Model Averaging for model-based bioequivalence design and analysis
      Model-based methods are expected to have higher power than non-compartment analysis (NCA) methods for evaluation of bioequivalence. However, model-based methods may lead to inflated type I error.  Further, model building during model-based bioequivalence analysis may introduce bias. We have developed a model-based method that performs a trial simulation step based on parameter and model uncertainty (using model-averaging methods) to obtain confidence intervals for ratios of PK metrics (AUC and Cmax). Generally, the model-based approaches showed acceptable overall type I error and higher power compared to standard methods. For sparse data experiments, further improvements in the model-based performance could be seen by optimizing designs (optimal designs for model-averaging).
    2. Simon Buatois – Extension of MCP-MOD for longitudinal data
      Finding the right dose remains a crucial milestone in drug development. In 2014, the EMA organized a workshop re-emphasizing that dose-finding should rely on “model-based estimation, rather than hypothesis testing via pairwise comparisons”. In this context, the multiple comparison procedures and modelling (MCP-MOD) technique received an EMA and FDA qualification opinion as an efficient statistical methodology for design and analysis of dose-finding trials. MCP-MOD is a two-step approach, which first establishes the evidence of a drug effect, and then estimates the phase III dose. The objective of this work was to extend MCP-MOD to use longitudinal nonlinear mixed effects models in both steps.
    3. Stephen J. Ruberg – WHAT is the Big Deal (!) (?)
      Clinical research studies have become more complex as the medical research community, including pharma, have tackled more difficult/chronic diseases, used more innovative trial designs and studied diverse treatment modalities. This complexity has led to greater complication in the evaluation of a treatment effect, even to the point of disagreements on the definition of WHAT a treatment effect is and, subsequently, how it should be estimated. This talk will describe how the ICH-E9(R1) Addendum on estimands approaches this issue and will challenge some traditional perspectives on defining WHAT a treatment effect is. Some proposals for novel thinking will be shared.
    4. Mick Looby – Estimands: a pharmacometrics perspective
      The new “estimand framework” in clinical drug development – as proposed in the ICH E9 (R1) guideline – highlights the importance of a precise definition of the treatment effect of interest to improve planning, design, analysis and interpretation. An important example is the method-effectiveness (ME), i.e., the causal estimate of drug response. Depending on the clinical setting, unbiased ME estimation is often complicated by intercurrent events and may require extensive assumption rich methods. The estimand framework acknowledges that pre-specified subject-matter based assumptions may be required for valid statistical estimation. This feature should facilitate the pharmacometric approach in drug development, for instance for therapy optimization.

PBPK modelling as a tool for extrapolation to special populations

Chairs: Oscar Della Pasqua, Tobias Kanacher

PBPK has emerged as an important tool in drug development. In fact, it is usually performed when data arising from clinical scenarios are integrated to predict unknown or untested scenarios. Typical applications include extrapolation from adults to children or from healthy to diseased populations. The predictive performance of such extrapolations depends on the integration of knowledge regarding the differences and similarities in the anatomy, pharmacology and physiology across populations. However, this knowledge is often scarce or absent for populations excluded from the typical critical path of drug development.

This session aims at identifying current knowledge gaps for predicting drug exposure and response in special populations, for which data are limited during clinical drug development. Further, it should provide the audience with food for thought and innovative solutions to overcome known limitations.


      1. Erik Sjögren – A PBPK Framework to Predict Drug Exposure in Malnourished Children
        Malnutrition in children is a global health problem, particularly in developing countries. The nutritional status impacts body composition and physiological functions, which leads to altered drug disposition and effect in this already vulnerable population. PBPK modelling can predict the effect of starvation by linking physiological changes to pharmacokinetic (PK) consequences. Due to, lacking information on body composition and the scarce availability of controlled clinical trials in malnourished children, a generic PBPK model in this population is lacking. By combining information on a) the differences in body composition between healthy and malnourished adults and b) the differences in physiology between healthy adults and children, a physiologically based bridge to a malnourished pediatric population can be made.
        In this talk we will 1) show the current approach to derive changes to body composition and plasma protein concentrations due to protein energy malnutrition for adults from the literature and implemented data in PK-Sim®. 2) We will also provide case examples of applying the implemented strategy where PBPK drug models were developed and verified with clinical data for adults and then used to predict plasma concentration time profiles, including interindividual variability, in malnourished children.
      2. Thorsten Lehr – PBPK modelling of polypharmacy in a genetic heterogeneous populations
        Drug–drug (DDIs) and drug–gene interactions (DGIs) pose a serious health risk that can be avoided by dose adaptation. These interactions are investigated in strictly controlled setups, quantifying the effect of one perpetrator drug or polymorphism at a time, but in real life patients frequently take more than two medications and are very heterogeneous regarding their genetic background and ignoring complex drug-drug-gene interactions (DDGI) can be very dangerous for the patient. PBPK models are excellent tools to predict the DDGI potential of drugs in silico and allow development of alternative dosing regimens for patients.
      3. Kevin Watt – PBPK modelling with extracorporeal life support: predicting cefepime dosing in children on continuous renal replacement therapy
        Extracorporeal life support (ECLS) is a life-saving technology in critically ill children. Children supported with ECLS (e.g., ECMO, dialysis) receive numerous drugs to treat critical illness and the underlying disease. Unfortunately, the majority of drugs prescribed to children on ECLS lack dosing information. Dosing is different in this population because the ECLS circuit components, like filters and tubing, as well as physiologic alterations triggered by critical illness affect drug disposition substantially. The lack of appropriate dosing information can result in therapeutic failure and even death. Dose selection to achieve safe and effective use of drugs in children on ECLS is not feasible with traditional pharmacokinetic (PK) trials for two reasons: 1) the effect of ECLS on drug disposition is drug- and age-specific, necessitating trials for all possible drug-, age-, and ECLS circuit combinations; thus requiring large numbers of children; and 2) these trials would need to be repeated whenever new ECLS circuit equipment is developed to quantify the effect of the new equipment on dosing. Our team has developed an alternative approach that addresses these limitations by using PBPK mathematical models to translate benchside ECLS experiments into bedside dosing recommendations. Model predictions are then confirmed with opportunistic drug concentration data collected from children via a small, efficient PK trial. In this talk we will 1) review the methods to develop and qualify an ECLS PBPK model and 2) provide an example using cefepime administered to children on continuous renal replacement therapy.

Addressing Global Anti-infective Drug Development Using In Silico Tools and Interdisciplinary Modeling

Chairs: Karim Azer, Carl Kirkpatrick

Modelling and simulation of complex biological systems has advanced in differing scientific domains with domain-specific approaches and applications. For example, over the last several decades, pharmaceutical companies and global regulators have adopted pharmacometrics to inform the drug development process, leading to safer and more effective therapies. Interdisciplinary modelling approaches that combine and encompass pharmacometrics, health economics and outcomes research, and data sciences approaches can expand and enhance the impact of in silico methodologies within the pharmaceutical industry and public health policy making. This session’s talks that explain how interdisciplinary modelling has supported drug development to help fight global infectious diseases.


    1. Billy Amzal – Use of Innovative Trial Design and Advanced Modelling to Inform Policy: Thailand Case Study
      A Phase 3B trial for prevention of Mother-to-Child transmission of HIV was designed under challenging patients recruitment and limited resources. To leverage from large historical data, a Bayesian model-based meta-analysis was conducted to allow a single arm-trial design to test a new preventive protocol vs. historical control defining a standard of care. An adaptive decision scheme to stop or continue the trial was defined to minimize the expected sample size and trial duration. Successful results were reported decreasing the transmission rate by 1/3. Further health economic impact was assessed, and WHO guidelines on HIV prevention were amended accordingly.
    2. Nitin Baliga- A Multiscale Paradigm Shift for Winning the Arms Race against TB Antimicrobial resistance (AMR) is a growing threat for diseases like tuberculosis (TB).
      A paradigm shift is needed to formulate effective combination therapies to disrupt the interplay of stochastic switching, bet hedging, adaptive prediction, reversible physiological responses, and alternate metabolic states that underlie antimicrobial tolerance and the pathways they pave for emergence and sustenance of AMR. I will discuss strategies to formulate such therapies through technological and computational advancements for uncovering the complex interplay of host and pathogen networks –from a molecular level to systems scale, and from populations to single cell resolution.
    3. Geoff Garnett – Use of Modelling to Inform Anti-viral Drug Development and Enhance Global Health
      Mathematical models of the population dynamics of infectious disease offer insights into their epidemiology, evolution, and control, and provide a framework to calculate the cost-effectiveness of  anti-viral treatments. For 30 years mathematical models have been used to assess policies for the use of antiviral treatments, exploring the influence of combination treatment, the evolution of resistance, treatment as prevention, and pre-exposure prophylaxis, and continues to inform the development and introduction of new antiviral regimens. Key concepts in infectious disease epidemiology will be reviewed with reference to the predicted and observed impacts of antiretroviral use in the response to the HIV pandemic.
    4. Sunil Parikh – Treatment of Infectious Disease in sub-Saharan Africa: Integrating Clinical Care with Pharmacometrics, an example to optimize anti-malarial treatment in pregnancy and pediatric care
      Malaria remains the leading cause of single cause morbidity and mortality in young children in sub-Saharan Africa. Pregnant women and those with HIV also bear a heavy burden of disease. Over the past 15 years, the landscape of malaria treatment has seen a dramatic shift to the use of artemisinin-based combination therapies (ACTs). PK/PD studies in these vulnerable populations were largely absent at the time of ACT roll-out, and our group and others have been addressing this critical gap through the conduct of intensive and population PK/PD studies in Uganda and Burkina Faso. Our goal is to obtain data that can directly impact policy.

Talks from submitted abstracts

Chair: TBC



  1. Shan Pan – Therapeutic drug monitoring of adalimumab in psoriasis: integrated statistical and pharmacometric analyses using real-world data.
    Adalimumab, a TNF-alpha blocker and a first-line biologic therapy for psoriasis, has been used widely for patients with moderate to severe plaque psoriasis, although there is significant heterogeneity in response. The current study, based on large clinical data from a real-world setting, aimed to evaluate a proactive therapeutic drug monitoring (TDM) strategy for adalimumab using pharmacometric modelling and simulation, given the therapeutic range defined from statistical analysis. Using real-time stochastic simulation the proactive TDM strategy, in comparison to standard of care, was evaluated and this provided better clinical responses requiring higher dose costs. Ultimately, adalimumab doses could be adjusted using a Bayesian TDM algorithm for individual patients with psoriasis.
  2. Babajide Shenkoya – Modeling the Impact of Adherence on Antiretroviral drugs concentration in the Lymphoid Tissues of HIV-positive Pregnant Women.
    Lymphoid tissues (LT) constitute a significant HIV reservoir, there is limited data on antiretroviral penetration into LT. We aim to estimate ARV concentration in LT of HIV-positive pregnant women (HPPW) using whole-body PBPK under different adherence scenarios.
    We assessed adherence and viral loads (VL) in 187 HPPW, study was approved by clinicaltrials.gov (NCT03284645). PBPK model was built with Simbiology (MATLAB R2018b) to describe ARV flow into LT. ARV concentration in LT were compared with 95% Inhibitory Concentration (IC95).
    VL ≤ 1,000 cpm was associated with low risk of HIV vertical transmission (aOR 0.846, 95% CI: 0.719 – 0.996, p = 0.045). The LT-to-plasma ratio for EFV, DTG and RPV were 74.8, 0.27 and 5.17 respectively, patients missing doses consecutively have lowest LT penetration.
    High adherence is required for exposing HIV to lethal ARVs in tissue reservoirs. PBPK is a useful tool in estimating LT concentration of antiretrovirals.
  3. Antonio Gonçalves –  Modeling HBV dynamics in patients treated with capsid assembly modulators.
    Hepatitis B RNA-containing particles (HBV RNA) are encapsidated pre genomic RNA (pgRNA) whose production is blocked by capsid assembly modulators. To tease out the determinants of HBV RNA dynamics, we developed a novel model of HBV integrating intracellular and extracellular processes of HBV replication. We showed that CAM strongly reduces HBV RNA and HBV DNA in biphasic manner. However, HBV RNA and HBV DNA differed by biological process limiting their first phase of decline i.e.  clearance and secretion, respectively. The model might be used in the future to better understand HBV RNA kinetics with different treatment strategies.
  4. Thanaporn Wattanakul – A semi-mechanistic pharmacokinetic and pharmacodynamic model of piperaquine in healthy volunteers with induced blood-stage P. falciparum malaria infection.
    Understanding the in vivo PK/PD properties of drugs is essential for rational dose-optimization and selection. Induced blood-stage malaria infection studies are ideal to characterize these properties for antimalarial drugs. The aim of this study was to develop a semi-mechanistic PK/PD model describing piperaquine and its parasite dynamics in healthy volunteers with induced P. falciparum blood-stage malaria. The developed semi-mechanistic model successfully incorporated many of the biological processes present during a natural infection, including parasite maturation, parasite sequestration, infection synchronicity, and parasite multiplication. This will be a useful tool to assess novel antimalarial drug combinations, and its impact on multidrug resistant infections.
  5. Samuel Callisto – An Empirical Approach to Identifying Electrophysiological Correlates of Topiramate-Related Working Memory Impairment for Pharmacokinetic-Pharmacodynamic Modeling
    Discerning the root cause of adverse effects of a medication can be difficult when a drug, such as the anti-seizure agent topiramate (TPM), affects multiple biological processes. In this study, a population PK-PD model was developed to quantify the relationship between TPM plasma concentration and electroencephalographic (EEG) measures related to working memory (WM). Post hoc sparse principal component analysis (sPCA) was conducted to determine which EEG components were most associated with TPM-related WM impairment. sPCA was able to identify a composite EEG-derived index of TPM-related WM impairment which (i) could be used downstream in PK-PD modeling to quantify drug effects on multiple processes, and (ii) verified that the EEG index selected correlated with TPM-related behavioral deficits.

Target concentration intervention – time for intervention

Chairs: Nick Holford, Stefanie Hennig

The target concentration intervention (TCI) concept was described 20 years ago. Since then, clinical trials of TCI have been shown to be superior to therapeutic drug monitoring (TDM). Despite the strong scientific theory and clinical trial data favouring TCI there is still widespread use of TDM. The symposium will review evidence supporting the use of TCI including evaluations of TCI tools in practice. It will show how TCI tools can be used to improve patient outcome and safety by targeted dose individualization. Use of TCI instead of TDM can facilitate understanding and uptake of such tools to improve patient care.


    1. David Metz – TCI works but TDM does not: the mycophenolic acid story
      The evidence for concentration-controlled dosing (CCD) of mycophenolic acid (MPA) products has long been considered weak and contradictory. In fact, the RCT data clearly supports CCD, however the CCD method is critical. When MPA dose optimization is based on PK-guided TCI, exposure is effectively controlled, and clinical benefits are seen. When CCD is by individual clinician-based dose adjustment to a therapeutic range (TDM), benefit is limited if at all. Over the 20 years since its introduction, enthusiasm for MPA CCD rose and then fell away. Interrogation of the trial data show this to be a story of TCI versus TDM.
    2. Stefanie Hennig – Comparison of TCI dosing tools for busulfan
      The aim was to assess model-based dosing tools (InsightRX and NextDose) to estimate busulfan exposure in comparison to clinically used intensive sampling exposure estimation and using limited sampling strategies. The prediction of cumulative exposure (AUCcum ) by each dosing tool was compared to current clinical practice estimation, aiming for pre-defined individualised target exposure for each patient. In clinical practice TCI was successful with 84.2% of patients achieving an AUCcum within ±5 mg·h·L-1 of their individual targeted AUCcum. Both model-based dosing tools accurately and precisely estimated busulfan exposure under several limited sampling strategies, which provides evidence for prospective use of the tools in clinical practice.
    3. Nick Holford – NextDose – A clinically useful web based tool for TCI
      Target concentration intervention (TCI) is a challenge to apply in a clinical setting because it requires skills that are not in the expertise of typical clinical teams. NextDose is a web based (http://www.nextdose.org) dose individualization tool that implements TCI with an interface designed for clinicians to enter data and obtain dosing advice without having to cope with the details of theory and implementation. TCI based advice is available for busulfan, methotrexate, tacrolimus, warfarin, linezolid, voriconazole, vancomycin, amikacin, gentamicin and caffeine. The pharmacometric challenges for implementation of TCI will be discussed using examples from NextDose.
    4. Hesham Al-Sallami – Enoxaparin – a new target for TCI
      The aim is to assess the feasibility and clinical benefit of using a Bayesian dosing tool of enoxaparin compared to standard clinical dosing. Enoxaparin is a widely used anticoagulant with a narrow therapeutic window which warrants dose individualisation especially for obese patients and those with renal impairment. Patients were recruited at Dunedin Hospital, New Zealand and randomised to individualised or conventional dosing. Feasibility was assessed in terms of acceptability, implementation, and practicality. Suboptimal anti-Xa concentration was identified in 38% of patients following standard dosing. Bayesian dosing was feasible and helped in achieving anti-Xa target. However, restricting its use to hospital patients will limit its feasibility.
    5. Hidefumi Kasai – Dosage individualization for anticancer chemotherapy based on neutropenia probability predicted by Bayesian forecasting
      Neutrophil counts during an anticancer treatment, eribulin, were modeled using modified Friberg model, which gave better predictability for the lower values of the data.  Bayesian estimation with this model was performed using the 1st cycle data to predict the 2nd cycle profile and probability of Grade 3/4 neutropenia. This scheme can be used to individualize anticancer drug dosing to alleviate the toxicity.