Dose rationale for antibiotic combination therapy in infection diseases
Lead Research Organisation:
University College London
Department Name: Institute for Global Health
Abstract
Doctors need to use a complex range of drug combinations to cure many infectious diseases. A good example is tuberculosis (TB). Globally, 9 million people are living with TB. It causes about 1.5 million deaths per year - more than any other infectious disease.
Standard treatment involves four different anti-TB drugs, given in combination over six months - this is a long time to take drugs that cause a range of side effects. Many people do not complete the course of treatment, meaning that their TB may not be cured. They may also develop resistance to TB drugs.
Up to now, recommendations about the amount of drugs and length of treatment that should be offered to people with TB have been based mainly on observational experience, supported by expert opinions.
If we could shorten the length of time that people have to take anti-TB drugs, they are more likely to complete the course of treatment and respond to the drugs effectively. However, a number of multi million pound multi centre trials that have attempted to do this (for example by substituting the existing drugs with a drug called moxifloxacin) have failed.
Across a range of health conditions, the use of mathematical models (i.e. nonlinear mixed-effects models) is becoming increasingly common. These models help experts to make recommendations about the dose of drugs that should be offered, and how long this treatment should be offered for. However, at the moment there is no such model available for the treatment of TB.
This research proposal aims to address this problem. It will bring together two different types of data to develop a mathematical modelling framework:
1. drug concentrations in the blood and lungs of human beings
2. data from innovative in-vitro experiments, which can mimic the antibacterial drug effect in a human lung.
The combination of these two different types of data in a modelling framework is new. Because of this, a variety of nonlinear mixed-effects models and statistical methods will need to be tested to develop the most reliable framework.
This modelling framework can then be used to make recommendations about the level of anti-TB drugs which should be offered, and how long these should be offered for, personalised for patient populations (i.e. children and elderly). Eventually this will translate into personalised TB treatments, thus improving the quality of life for patients and saving healthcare time and resources.
If this project is successful, the modelling framework will also serve as the base for dose selection and personalisation of other drugs and in other infectious diseases. For example, the framework could inform the design of new clinical trials and the selection of doses of new anti-TB drug combinations for the treatment of multi-drug resistant TB.
The project will be based at University College London, and will be undertaken by Dr Frank Kloprogge. It will be hosted by Professor Oscar Della Pasqua, and involves collaborations with other researchers from University College London, St Andrews University and Université Paris Diderot.
Standard treatment involves four different anti-TB drugs, given in combination over six months - this is a long time to take drugs that cause a range of side effects. Many people do not complete the course of treatment, meaning that their TB may not be cured. They may also develop resistance to TB drugs.
Up to now, recommendations about the amount of drugs and length of treatment that should be offered to people with TB have been based mainly on observational experience, supported by expert opinions.
If we could shorten the length of time that people have to take anti-TB drugs, they are more likely to complete the course of treatment and respond to the drugs effectively. However, a number of multi million pound multi centre trials that have attempted to do this (for example by substituting the existing drugs with a drug called moxifloxacin) have failed.
Across a range of health conditions, the use of mathematical models (i.e. nonlinear mixed-effects models) is becoming increasingly common. These models help experts to make recommendations about the dose of drugs that should be offered, and how long this treatment should be offered for. However, at the moment there is no such model available for the treatment of TB.
This research proposal aims to address this problem. It will bring together two different types of data to develop a mathematical modelling framework:
1. drug concentrations in the blood and lungs of human beings
2. data from innovative in-vitro experiments, which can mimic the antibacterial drug effect in a human lung.
The combination of these two different types of data in a modelling framework is new. Because of this, a variety of nonlinear mixed-effects models and statistical methods will need to be tested to develop the most reliable framework.
This modelling framework can then be used to make recommendations about the level of anti-TB drugs which should be offered, and how long these should be offered for, personalised for patient populations (i.e. children and elderly). Eventually this will translate into personalised TB treatments, thus improving the quality of life for patients and saving healthcare time and resources.
If this project is successful, the modelling framework will also serve as the base for dose selection and personalisation of other drugs and in other infectious diseases. For example, the framework could inform the design of new clinical trials and the selection of doses of new anti-TB drug combinations for the treatment of multi-drug resistant TB.
The project will be based at University College London, and will be undertaken by Dr Frank Kloprogge. It will be hosted by Professor Oscar Della Pasqua, and involves collaborations with other researchers from University College London, St Andrews University and Université Paris Diderot.
Technical Summary
Parameterisation of pharmacokinetic and pharmacokinetic-pharmacodynamic (PKPD) data into nonlinear mixed-effects models is required to better understand the time course of bacterial growth and bactericidal activity of drugs. To date, little attention has been paid to how these models should be parameterised to quantify drug effects during combination therapy or explain variability associated with clinical, genetic and demographic covariate factors. In this proposal, in-vivo clinical data will be integrated with in-vitro data from innovative hollow fibre experiments using nonlinear mixed-effects modelling techniques to improve dose selection for drug combinations and to personalise the treatment of anti-infective agents in different patient groups. Moreover, the approach will be used to support the design of prospective clinical studies in tuberculosis, an area which has gained scientifically and regulatory relevance over the last decade.
In previous investigations, lung tissue/lesion concentrations and antibacterial activity has been described using empirical or more mechanistic and single-state or multi-state nonlinear mixed-effects models, respectively. Here, nonlinear mixed-effects modelling approaches will be identified that enable further characterisation of bacterial growth over time in experimental conditions which mimic disease in humans. The ultimate goal is to identify new parameterisations supported with prior distributions in a Bayesian framework which facilitate the translation of nonclinical data, enabling the prediction of treatment response in patients using the software package NONMEM (aim 1).
The availability of such a framework will allow the evaluation of simulation scenarios to optimise, personalise and recommend standard drug combination therapy for pulmonary tuberculosis (aim 2). In combination with optimality concepts, the proposed approach can be used to support the design of innovative and efficient prospective PKPD studies (aim 3).
In previous investigations, lung tissue/lesion concentrations and antibacterial activity has been described using empirical or more mechanistic and single-state or multi-state nonlinear mixed-effects models, respectively. Here, nonlinear mixed-effects modelling approaches will be identified that enable further characterisation of bacterial growth over time in experimental conditions which mimic disease in humans. The ultimate goal is to identify new parameterisations supported with prior distributions in a Bayesian framework which facilitate the translation of nonclinical data, enabling the prediction of treatment response in patients using the software package NONMEM (aim 1).
The availability of such a framework will allow the evaluation of simulation scenarios to optimise, personalise and recommend standard drug combination therapy for pulmonary tuberculosis (aim 2). In combination with optimality concepts, the proposed approach can be used to support the design of innovative and efficient prospective PKPD studies (aim 3).
Planned Impact
The four beneficiaries of the proposed research will be:
- Patients requiring a combination of antibiotic compounds to treat an infectious disease
- Clinicians and nurses
- For profit and not for profit organisations conducting pharmaceutical and clinical research and development for drug combinations to treat infectious diseases
- Medicine regulatory agencies and policy makers
The combination of isoniazid, rifampicin, pyrazinamide and ethambutol or moxifloxacin to treat pulmonary tuberculosis is selected as paradigm. Pulmonary tuberculosis patient, clinicians and nurses will eventually benefit from the research in that optimised and personalised treatment guidelines will be developed for the selected paradigm drugs. The developed modelling framework will enable to optimise the dosages of each active compound in order to maximise the total antibacterial activity of the treatment combination whilst minimising the course of duration. This will contribute to a lower risk of relapses, lower risk of resistance development and a longer therapeutic life time of the drug combination. This results into a reduction in the burden of expensive long follow-up programmes. Optimised and personalised treatments using the modelling framework will be communicated in the form of scientific papers in the third year of the fellowship.
Any for profit and not for profit organisation involved in updating dosing guidelines of existing drug combinations, by shortening the treatment duration and personalising dosing recommendations (e.g. for paediatric and elderly patients), or selection of dosages for novel drug combinations will be beneficiaries of this research. The proposed modelling framework, combines different types of data, and enriches the pharmacokinetic-pharmacodynamic information content. This enables rational dose selection for drug combination to treat diseases such as pulmonary tuberculosis what has been till date not possible. Due to the nature of the proposed research, the modelling framework provides the base for rational dose selection of novel antibiotic drug combinations to treat multi drug resistant tuberculosis or in other therapeutic areas.
At last, medicines regulatory agencies and policy makers such as, WHO, Public Health England and the British Thoracic Society, will benefit from this research through their role in both guiding the types of studies required, and in assessing submissions for product licensing.
- Patients requiring a combination of antibiotic compounds to treat an infectious disease
- Clinicians and nurses
- For profit and not for profit organisations conducting pharmaceutical and clinical research and development for drug combinations to treat infectious diseases
- Medicine regulatory agencies and policy makers
The combination of isoniazid, rifampicin, pyrazinamide and ethambutol or moxifloxacin to treat pulmonary tuberculosis is selected as paradigm. Pulmonary tuberculosis patient, clinicians and nurses will eventually benefit from the research in that optimised and personalised treatment guidelines will be developed for the selected paradigm drugs. The developed modelling framework will enable to optimise the dosages of each active compound in order to maximise the total antibacterial activity of the treatment combination whilst minimising the course of duration. This will contribute to a lower risk of relapses, lower risk of resistance development and a longer therapeutic life time of the drug combination. This results into a reduction in the burden of expensive long follow-up programmes. Optimised and personalised treatments using the modelling framework will be communicated in the form of scientific papers in the third year of the fellowship.
Any for profit and not for profit organisation involved in updating dosing guidelines of existing drug combinations, by shortening the treatment duration and personalising dosing recommendations (e.g. for paediatric and elderly patients), or selection of dosages for novel drug combinations will be beneficiaries of this research. The proposed modelling framework, combines different types of data, and enriches the pharmacokinetic-pharmacodynamic information content. This enables rational dose selection for drug combination to treat diseases such as pulmonary tuberculosis what has been till date not possible. Due to the nature of the proposed research, the modelling framework provides the base for rational dose selection of novel antibiotic drug combinations to treat multi drug resistant tuberculosis or in other therapeutic areas.
At last, medicines regulatory agencies and policy makers such as, WHO, Public Health England and the British Thoracic Society, will benefit from this research through their role in both guiding the types of studies required, and in assessing submissions for product licensing.
People |
ORCID iD |
Frank Kloprogge (Principal Investigator / Fellow) |
Publications
Abubakar I
(2017)
End of the Road for Adjunctive Vitamin D Therapy for Pulmonary Tuberculosis?
in American Journal of Respiratory and Critical Care Medicine
Boonsathorn S
(2019)
Clinical Pharmacokinetics and Dose Recommendations for Posaconazole in Infants and Children.
in Clinical pharmacokinetics
Carrara L
(2020)
Ethambutol disposition in humans: Challenges and limitations of whole-body physiologically-based pharmacokinetic modelling in early drug development.
in European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Chiesa R
(2020)
Proposed Therapeutic Range of Treosulfan in Reduced Toxicity Pediatric Allogeneic Hematopoietic Stem Cell Transplant Conditioning: Results From a Prospective Trial.
in Clinical pharmacology and therapeutics
Gastine S
(2021)
Systematic Review and Patient-Level Meta-Analysis of SARS-CoV-2 Viral Dynamics to Model Response to Antiviral Therapies.
in Clinical pharmacology and therapeutics
Kloprogge F
(2021)
Exploring a combined biomarker for tuberculosis treatment response: protocol for a prospective observational cohort study
in BMJ Open
Kloprogge F
(2020)
Longitudinal Pharmacokinetic-Pharmacodynamic Biomarkers Correlate With Treatment Outcome in Drug-Sensitive Pulmonary Tuberculosis: A Population Pharmacokinetic-Pharmacodynamic Analysis.
in Open forum infectious diseases
Kloprogge F
(2019)
Revising Pediatric Vancomycin Dosing Accounting for Nephrotoxicity in a Pharmacokinetic-Pharmacodynamic Model.
in Antimicrobial agents and chemotherapy
Kloprogge F
(2022)
Understanding emergence of antimycobacterial dose dependent resistance
Description | AMR Seed Fund |
Amount | £15,000 (GBP) |
Funding ID | NIHR200652 |
Organisation | National Institute for Health Research |
Sector | Public |
Country | United Kingdom |
Start | 01/2021 |
End | 03/2022 |
Description | AMS Seed Fund |
Amount | £15,000 (GBP) |
Organisation | National Institute for Health Research |
Sector | Public |
Country | United Kingdom |
Start | 01/2021 |
End | 03/2022 |
Description | An Open-Source Database For Predicting Pharmacokinetics |
Amount | £49,758 (GBP) |
Funding ID | 214464/Z/18/Z |
Organisation | Wellcome Trust |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 07/2019 |
End | 07/2020 |
Description | London & Seoul: Harmonisation Of Tuberculosis Cohorts For Prediction Of Treatment Outcome |
Amount | £9,312 (GBP) |
Funding ID | MC_PC_18065 |
Organisation | Medical Research Council (MRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2019 |
End | 12/2020 |
Description | Rational Based Drug, Dose And Regimen Selection For Antimicrobial Drug Combinations |
Amount | £60,000 (GBP) |
Funding ID | 1905682 |
Organisation | Shionogi & Co., Ltd. |
Sector | Private |
Country | Japan |
Start | 01/2019 |
End | 12/2021 |
Description | sir Henry Dale Fellowship |
Amount | £1,113,271 (GBP) |
Funding ID | 220587/Z/20/Z |
Organisation | Wellcome Trust |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 09/2020 |
End | 09/2025 |
Title | PKPDai |
Description | A dataset with an indicator of likelihood it containing pharmacokinetic information. |
Type Of Material | Database/Collection of data |
Year Produced | 2020 |
Provided To Others? | Yes |
Impact | Interest amongst pharmacological modellers in that they can quickly obtain pharmacokinetic literature on drugs of interest. Even more interest in plans to expand the project with standardised and AI/ML curated open source data sets from literature. Several pharmaceutical industries have expressed interest and we currently explore options to get industry funding. |
URL | https://app.pkpdai.com/ |
Description | An open-source database for predicting pharmacokinetics |
Organisation | BenevolentAI |
Country | United Kingdom |
Sector | Private |
PI Contribution | Pharmacological expertise Supervision of PhD student (as subsidiary supervisor) Labelling of pharmacological literature |
Collaborator Contribution | Clinical pharmacy, machine learning and artificial inteligence expertise Supervision of PhD student (as subsidiary supervisor) Labelling of pharmacological literature |
Impact | An internet site (http://pkpdai.com/) Various oral presentations at national conferences 2 accepted abstracts at international concferences (South Africa and Slovenia) |
Start Year | 2019 |
Description | HFS experiments at UCL |
Organisation | University College London |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Initially HFS experiments were planned at St Andrews (by continuation of an existing collaboratio) as proposed in the initial fellowship proposal. Cat3 facilities in St Andrews broke down and a contigency plan was put in place. I will provide my expertise on running the hollow-fibre experimental setup to the UCL Centre for Clinical Microbiology. Moreover, this enables me to gain hands on-experience on microbiology. |
Collaborator Contribution | UCL Centre for Clinical Microbiology offers lab space and microbiology expertise. |
Impact | In-vitro hollow fibre experimental setup now at UCL. |
Start Year | 2017 |
Description | TB-Practecal |
Organisation | Médecins Sans Frontières (MSF) |
Country | France |
Sector | Charity/Non Profit |
PI Contribution | I have supported the team in designing a nested PK-study in an ongoing clinical trial through optimising the sampling design. |
Collaborator Contribution | The protocol amendment will generate new PKPD data in human for MDR-TB regimens which I could use for my resarch project. |
Impact | The protocol amendment for a nested PK study. |
Start Year | 2017 |
Description | UK-Korean partnership for a TB cohort |
Organisation | Catholic University of South Korea |
Country | Korea, Republic of |
Sector | Academic/University |
PI Contribution | We, as in I as PI with the UCL team together with the Korean team that is led by Jusang Kim. This award is all around building capacity to harmonise protocols for TB patient cohort studies in London and Seoul First visit to Seoul was in November. Training needs and action points have been identified. We are currently working on them and had initially planned a visit to London for laboratory staff to be trained and to undertake further brainstorms for proposal writing. This is however put on hold due to the corona virus outbreak. |
Collaborator Contribution | Partners have identified local collaborators in each of the specific disciplines that are willing to collaborate on the proposed TB patient cohort study. |
Impact | Lab training for Korean technicians in London to harmonise protocols. |
Start Year | 2019 |
Title | An open-source database for predicting pharmacokinetics |
Description | Our aim is to create a web resource for academic and industry modellers in the field of pharmacology to share standard big datasets and methodological techniques. The ultimate goal with the output is to encourage open research practices in clinical pharmacology through providing the data resources needed to make a shift from a reductionist (semi) mechanism-based approach to a machine learning based approach for modelling of dose-concentration-response. The completed project will be a website at the address: www.pkpdai.com. The first dataset will focus on the pharmacokinetic parameters clearance (CL) or CL/F, for non-intravenous administration, where F is the bioavailability. We are developing a system to automatically expand this dataset as new papers are published. A data dictionary will be provided and simple data querying and plotting tools made available via R Shiny. The website will then have the capacity to grow by adding new datasets and model code. |
Type Of Technology | Webtool/Application |
Year Produced | 2019 |
Open Source License? | Yes |
Impact | We have noticed steady traffic on the internetsite and Pharma industry has expressed industry to further fund this innitiative. |
URL | https://www.ucl.ac.uk/global-health/research/z-research/open-source-database-predicting-pharmacokine... |
Title | PKPD converter for HFS experiments |
Description | A web application readily converting pharmacokinetic parameters into flow rates for peristaltic pumps for the hollow fibre infection model. |
Type Of Technology | Webtool/Application |
Year Produced | 2018 |
Open Source License? | Yes |
Impact | The web application is currently under peer review as part of a manuscript submitted to scientific reports in order to increase its visibility amongst scientists in the field. |
Description | An interview with Voice of America |
Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Media (as a channel to the public) |
Results and Impact | An interview with the voice of America on a publication. |
Year(s) Of Engagement Activity | 2018 |
URL | https://www.voanews.com/a/malaria-drug-children-pregnant-women/4435861.html |
Description | Invited presentation at HFIM workshop |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Invited talk about hollow fibre experiments for antimicrobial drug combination research. |
Year(s) Of Engagement Activity | 2019,2020 |
Description | Invited speaker at World TB Day Symposium 2018 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Presentation of my work at the World TB day |
Year(s) Of Engagement Activity | 2018 |
URL | https://panopto.lshtm.ac.uk/Panopto/Pages/Viewer.aspx?id=5ddad554-12f4-42e3-bab8-8bdc83ecbfb2 |
Description | Pharmacological modelling course in Nairobi, Kenya |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Postgraduate students |
Results and Impact | Parameterisation of pharmacological data in mathematical models has become key in dose selection. Several not for profit organisations in Kenya (e.g. KEMRI-Wellcome Trust research programme, DNDi and local Universities) generate pharmacological data as part of (pre-)clinical studies although analyses remain outsourced to other parties, often in Europe. Together with a colleague at UCL I went to Nairobi to train local scientist in mathematical modelling applied to pharmacological data in order to support local capacity building and to allow them all analyses in house. The training aimed at the basics and participants were enthusiastic and told us they would love to see this meeting occurring next again at an intermediate level. |
Year(s) Of Engagement Activity | 2018 |