LABORATORY INFORMATICS g


together to help improve decision making for new therapeutics. One can now evaluate how the virus enters cells, how it interacts and replicates, what the immune system is doing in response, via sophisticated quantitative pharmacology frameworks and predictive tools and then simulate new situations, he suggested. ‘We can take huge amounts of data


from preclinical models, in vitro testing and clinical experience, as a fundamental foundation on which to use math engines to model what will happen in different trial scenarios, start to simulate clinical trials accurately, and then add data derived from new trials back into the model, and validate in silico learning,’ said Rayner. Certara modelling and simulation


software and services are harnessed by the pharmaceutical industry globally, together with regulators and academic institutions, Rayner continued. ‘Our in silico tools have evolved through a combination of learning through historical experimentation, and the ability to apply ‘math’ and modelling across the entire disease process.’ The ultimate aim is to develop more effective treatments, while also reducing the attrition rate, development timeline and associated costs. Central to this suite of in silico predictive


and intelligence tools is the firm’s Simcyp Simulator, which is applied at multiple points in the drug development and trials timeline to help determine optimum drug dose. This is critical to ensure that enough of a given drug is given to achieve efficacy, but that overdosing is avoided, both to reduce wasting the drug, and also to reduce the likelihood of drug-related side effects. The Simcyp Simulator has been


developed as a suite of modules that simulate drug pharmacokinetics (PK) and so can predict and describe how the body affects the drug-drug absorption, distribution, metabolism and excretion (ADME), and how PK may be altered by formulation, patient variables like age,


gender or genotypic information, or concomitantly administered medications. The Simcyp Simulator links laboratory


data to in vivo ADME data and when integrated with and extended to pharmacodynamic (PD) information (how the drug effects the body) such as biomarkers or clinical efficacy and safety, is a powerful tool to support dosing decision making in new trials.


‘Designing and running clinical trials for


any drug or vaccine is hugely expensive and time consuming, so there is a great need to boost efficiency, and improve the likelihood of success,’ commented Keith Nieforth, senior director, software division. ‘The Certara tools can also model drug


activity at particular sites of action, and look at the physicochemical properties of that molecule in the context of other molecules with similar structure and activity, to make predictions on whether the drug will reach target tissues, such as the lung, if we consider SARS-CoV-2,’ Nieforth said. ‘In the case of Covid-19 drug


development, the Certara models integrate simulations of drug pharmacokinetics and pharmacodynamics, alongside virus


“Our aim has been to develop these tools to help explore, quantify, predict, and confirm”


 Keith Nieforth 22 Scientific Computing World Summer 2020


interaction with the host and symptoms. ‘You can then link those models together and that enables you to simulate what you think might happen in clinical trials. Ultimately, modelling and simulation can reduce the number of patients, or trial arms required, as well as evaluate the influence of other design factors on trial outcomes, and so improve the probability of success,’ added Nieforth. In the race to develop therapeutics against COVID-19, the Simcyp Simulator has been instrumental in predicting whether it will be possible to achieve a therapeutic dose of existing drugs that were reportedly effective against the virus in the lab. ‘Hydroxychloroquine was shown to inhibit SARS-CoV-2 in lab-grown cells, but there were concerns that the plasma concentrations wouldn’t be high enough to achieve therapeutic efficacy in infected people,’ Rayner commented. ‘However, the virus accumulates in the lungs, and our simulation demonstrated that the concentration of drug that would reach the lungs would be sufficient to inhibit the virus.’ ‘That data has been instrumental in opening the way to hydroxychloroquine trials that are now ongoing worldwide,’ he


 Craig Rayner


added. ‘The drug worked in vitro and there’s pharmacological plausibility and there’s proof of hope that it would work because it would achieve the concentrations that we think are relevant to the site of primary viral action. Within the Accelerator, we worked with the University of Washington and New York University primary investigators, to achieve hydroxychloroquine dose selection, clinical study design and institutional review board approval, in just eight days, a process that would normally take months.’ Another existing drug that had shown


potential against SARS-CoV-2 in vitro was the antiparasitic drug ivermectin, Rayner continued. ‘This is the flipside to our experience with hydroxychloroquine, as in this case Simcyp simulations indicated that the drug would not achieve sufficient lung concentration to be effective against the virus in vivo, and we argued against the start of clinical trials that would likely fail.’ ‘We are learning from data emerging when is the most effective treatment point during the infection timeline. Give an antiviral drug too late, for example, and the virus has effectively finished its infectivity, and the patient is now dealing primarily with the immunological effects of the infection, which is almost a completely different ‘disease.’ What we are trying to do is to put models around viral kinetic data that is coming in from around the world, to understand the infection and viral load time course, and so simulate when to most appropriately effect therapy.’ ‘Our aim has been to develop these


tools to help explore, quantify, predict, and confirm,’ Nieforth said. ‘The endpoint is that they can be used together to provide recommendations that have impact. Appreciating that we have a very diverse user base, we have evolved our tools to cover the full scope of drug development and user experience, both for individual pieces of the workflow and also to tie those individual pieces together very nicely.’


@scwmagazine | www.scientific-computing.com


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