September 25, 2018

QSP for AMR: Modelling how the drugs get into the bugs

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What's quantitative & systems pharmacology?

“Quantitative and Systems Pharmacology (QSP) is an emerging discipline focused on identifying and validating drug targets, understanding existing therapeutics and discovering new ones.”-Quantitative and Systems Pharmacology in the Post-genomic Era: New Approaches to Discovering Drugs and Understanding Therapeutic Mechanisms.

AMR is gaining increasing importance in healthcare settings. But what’s AMR?

Antibiotics interfere with the complex “machinery” inside the bacteria for example by interfering with its metabolism, slowing down their growth significantly, so they are less of a thread. Other antibiotics target DNA which prevents from replicating and prevent bacteria from multiplying ultimate killing them. Or by simply reaping the outer layer of bacteria to shred so their inside spill out dying quickly all of this without bothering body cells.

But now evolution is making things more complicated, by small random change, a small amount of the bacteria might find a way to protect themselves. For example, by intercepting the antibiotic and change the molecule so it becomes harmless or by investing energy in pumps that eject the antibiotic before they can do damage.

Bacteria have two kinds of DNA the chromosome and small floating parts called plasmid with which they can exchange useful immunities or in a process called transformation bacteria can harvest dead bacteria and collect DNA pieces. This even works between different bacteria species and can lead to superbugs: bacteria that are immune to multiple kinds of antibiotics. A variety of superbugs already exist in the world especially hospitals are the perfect breeding grounds for them.

As a society, we have to change habits on the use of antibiotics and keep them as a last resort drug. In addition, interdisciplinary research is needed to keep developing new antibiotics.

Additional topics:

XChem: new experimental opportunities for testing theory

This team is making breakthrough discoveries in the fields of macromolecular crystallography, imaging and microscopy, biological cryo-imaging, magnetic materials, structures and surfaces, spectroscopy, and crystallography, which are generating high-throughput data that may accelerate the discovery of new medicines.

Moreover, they are committed to open data standards and all 3D structures of human proteins that are being elucidated are published for data analyst to test potential novel therapies in-silico. Cancer related proteins including human protein kinases, metabolism-associated proteins, integratl membrane proteins and proteins associated with epigenetics are the focus of the team and more information can be found in their website.

Pharmacokinetic–Pharmacodynamic Modeling in Pediatric Drug Development, and the Importance of Standardized Scaling of Clearance

Since modelling can be used readily to extrapolate results in adults to children hereby avoiding clinical trials in children there is a huge interest by all stakeholders to clarify when that is an appropriate practice. In principle, extrapolation should be done whenever is reasonable to assume that children in comparison to adults have a similar disease progression, response to intervention, exposure-response. However, if the exposure-response is dissimilar but there is a PD measurement that can be used to predict efficacy in children it would is still possible to conduct partial extrapolation. The decision tree below summarises the idea:

Decision tree pediatrics (E. Germovsek et al.)

The history of paediatrics did not start taking into consideration the complex maturation that occurs in human beings. Instead, early in time dose was simply scaled down linearly with weight. This wrong practice lead to the occurrence of serious adverse event such as the gray baby syndrome and kernicterus. One of the first achievements in modelling the dose in paediatrics was the use of Body Surface Area, Crawford et al., which improves dramatically the efficacy and safety profile. More recently a combination of allometric weight scaling with a sigmoidal function has been proposed to describe the changes in Cl due to age and weight:


On the other hand, for extrapolation, we are instead aiming for the use of modelling techniques that comprises individual variation. A prominent example of that is Non-Linear-Mixed Effect Modelling (NLME) where all the study data are fitted simultaneously in one model, but the PK parameters may vary between individuals (VBI). This approach has become standard practice because it provides unbiased estimates through simultaneous estimation of parameter-level interindividual variability and observation level residual variability.

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