Certara, the global leader in model-informed drug development and regulatory science, announced that it has published two papers demonstrating proof of concept for its Virtual Twin technology.
“Our Virtual Twin technology will allow clinicians to predict the optimal drug dosing regimen for an individual patient – one that maximizes therapeutic benefit while minimizing side effects – by evaluating the impact of different drug doses, schedules, and combinations in the patient’s in silico ‘virtual twin’ first,” explained Certara chief scientific officer Professor Amin Rostami, PharmD, PhD, FCP.
“The current drug development paradigm cannot study all the possible permutations of co-morbid conditions that a patient may possess. That is the beauty of Virtual Twin, it allows us to model a specific patient, the diseases that person is experiencing, and the likely impact that a specific drug will have on that individual instead of assuming that they will respond in the same way as the average person described on the drug label,” said Professor Rostami.
Certara’s Virtual Twin technology creates a computer-simulated model of each patient, replicating the patient’s various attributes that affect a drug’s fate in their body and hence its effects. These attributes include the patient’s age, weight, height, sex, ethnicity and genetics of drug metabolizing enzymes and drug transporters. The Virtual Twin model can also reflect the patient’s current drug dosage, fed or fasted state, co-morbid conditions and co-medications that affect the activity of certain metabolic enzymes and transporters, and their level of organ function.
As one can imagine, these cover a vast array of drug treatment scenarios where the combination of factors might not have been studied, leaving a knowledge gap when trying to decide the most appropriate drug dose for a given patient considering the combination of all the underlying conditions. Hence, it is not surprising that the recently published examples of applying Virtual Twin technology cover diverse areas.
In the first case, Tom Polasek, MD, PhD, a clinical pharmacologist at Certara Strategic Consulting, described in the British Journal of Clinical Pharmacology how he and his team employed Certara’s Simcyp Simulator to create ‘virtual twins’ and predict olanzapine exposure in individual patients. They adapted physiologically-based pharmacokinetic modeling and simulation (PBPK M&S) to implement model-informed precision drug dosing. Olanzapine is an antipsychotic drug that is used to treat schizophrenia and bipolar disorder.
Dr. Polasek’s team began by validating their olanzapine PBPK model against PK studies and data from therapeutic drug monitoring. They then used the Simcyp Simulator healthy volunteer population file to create ‘virtual twins’ of 14 patients. Single-dose studies were conducted and the olanzapine systemic exposure predicted in the virtual twins was compared with the actual drug concentrations measured in the corresponding patients. Those predicted exposures were also used to calculate a hypothetical decrease in exposure variability after the olanzapine dose was adjusted.
Certara’s Virtual Twin technology accurately predicted olanzapine PK parameters in the patients included in the single-dose studies. They represented healthy Caucasian, healthy Chinese, and geriatric Caucasian patients. Furthermore, the variability in olanzapine exposure following hypothetical dose-adjustment guided by PBPK M&S was two-fold lower than a fixed-dose regimen.
The second published application of the ‘virtual twin’ approach comes from the cardiac drug safety arena, identifying patients who could be at higher risk.
Nikunjkumar Patel, a principal scientist in Certara’s modeling and simulation group, and his team developed an in silico quantitative systems toxicology (QST) model for citalopram to serve as a ‘virtual twin’ and help predict the likely occurrence of cardiotoxic events in real patients under different clinical conditions. Citalopram is a widely prescribed antidepressant drug, which has been linked with cardiac toxicity at higher doses.
Their QST model, which is described in the AAPS Journal, was developed by combining in vitro cardiac ion channel current inhibition data with Certara’s biophysically-detailed model of human cardiac electrophysiology. This model factors in the effects of citalopram and its most electrophysiologically-active primary and secondary metabolites (desmethylcitalopram and didesmethylcitalopram), inhibition of multiple ion currents (IKr, IKs, ICaL), and the presence of unbound plasma citalopram.
The model’s predictive performance was verified using three therapeutic and supra-therapeutic drug exposure clinical cases. This study demonstrated that QST models, with the appropriate drug and systems parameters, can bridge the gap between preclinical cardiac safety assessments and clinical toxicology results.
Certara’s Virtual Twin technology has the power to accurately predict a drug’s behavior in sub-groups of patients who may not be considered ‘average.’ This permits dosing regimens to be optimized for individual patients, factoring in safety issues such as cardiac events, beyond what is covered during drug development using stratified patient groups and stated in the drug label.
Certara demonstrates proof, its virtual twin technology
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