Precision Medicine Is Not Just Genomics: The Right Dose for Every Patient.

Genomics has helped to initiate the era of precision medicine, with some drugs now prescribed on the basis of molecular genetic tests that indicate which patients are likely to respond or should not receive a drug because of a high risk of adverse effects. However, for precision medicine to realize its potential, the patient's history, environment, and lifestyle must also be taken into account. Improving precision medicine requires a better understanding of the underlying reasons for the variability in drug response so as to better identify which drug or combination of drugs is likely to be most effective for an individual patient, along with consideration of the optimal dose or doses for that patient. Greater individualization of dose will be an important means to achieve more precise medicine and mitigate significant variability in drug response. Achieving this will require changes in how drugs are developed, approved, prescribed, monitored, and paid for. Each of these factors is discussed in this review.

Beyond Population-Level Targets for Drug Concentrations: Precision Dosing Needs Individual-Level Targets that Include Superior Biomarkers of Drug Responses.

The purpose of precision dosing is to increase the chances of therapeutic success in individual patients. This is achieved in practice by adjusting doses to reach precision dosing targets determined previously in relevant populations, ideally with robust supportive evidence showing improved clinical outcomes compared with standard dosing. But is this implicit assumption of translatable population-level precision dosing targets correct and the best for all patients? In this review, the types of precision dosing targets and how they are determined are outlined, problems with the translatability of these targets to individual patients are identified, and ways forward to address these challengers are proposed. Achieving improved clinical outcomes to support precision dosing over standard dosing is currently hampered by applying population-level targets to all patients. Just as "one-dose-fits-all" may be an inappropriate philosophy for drug treatment overall, a "one-target-fits-all" philosophy may limit the broad clinical benefits of precision dosing. Defining individual-level precision dosing targets may be needed for greatest therapeutic success. Superior future precision dosing targets will integrate several biomarkers that together account for the multiple sources of drug response variability.

Development and application of neonatal physiology-based pharmacokinetic models of amikacin and fosfomycin to assess pharmacodynamic target attainment.

Antimicrobial resistance increasingly complicates neonatal sepsis in a global context. Fosfomycin and amikacin are two agents being tested in an ongoing multicenter neonatal sepsis trial. Although neonatal pharmacokinetics (PKs) have been described for these drugs, the physiological variability within neonatal populations makes population PKs in this group uncertain. Physiologically-based pharmacokinetic (PBPK) models were developed in Simcyp for fosfomycin and amikacin sequentially for adult, pediatric, and neonatal populations, with visual and quantitative validation compared to observed data at each stage. Simulations were performed using the final validated neonatal models to determine drug exposures for each drug across a demographic range, with probability of target attainment (PTA) assessments. Successfully validated neonatal PBPK models were developed for both fosfomycin and amikacin. PTA analysis demonstrated high probability of target attainment for amikacin 15 mg/kg i.v. q24h and fosfomycin 100 mg/kg (in neonates aged 0-7 days) or 150 mg/kg (in neonates aged 7-28 days) i.v. q12h for Enterobacterales with fosfomycin and amikacin minimum inhibitory concentrations at the adult breakpoints. Repeat analysis in premature populations demonstrated the same result. PTA analysis for a proposed combination fosfomycin-amikacin target was also performed. The simulated regimens, tested in a neonatal sepsis trial, are likely to be adequate for neonates across different postnatal ages and gestational age. This work demonstrates a template for determining target attainment for antimicrobials (alone or in combination) in special populations without sufficient available PK data to otherwise assess with traditional pharmacometric methods.

Using Machine Learning to Individualize Treatment Effect Estimation: Challenges and Opportunities.

The use of data from randomized clinical trials to justify treatment decisions for real-world patients is the current state of the art. It relies on the assumption that average treatment effects from the trial can be extrapolated to patients with personal and/or disease characteristics different from those treated in the trial. Yet, because of heterogeneity of treatment effects between patients and between the trial population and real-world patients, this assumption may not be correct for many patients. Using machine learning to estimate the expected conditional average treatment effect (CATE) in individual patients from observational data offers the potential for more accurate estimation of the expected treatment effects in each patient based on their observed characteristics. In this review, we discuss some of the challenges and opportunities for machine learning to estimate CATE, including ensuring identification assumptions are met, managing covariate shift, and learning without access to the true label of interest. We also discuss the potential applications as well as future work and collaborations needed to further improve identification and utilization of CATE estimates to increase patient benefit.

Artificial intelligence assessment of the potential of tocilizumab along with corticosteroids therapy for the management of COVID-19 evoked acute respiratory distress syndrome.

Acute respiratory distress syndrome (ARDS), associated with high mortality rate, affects up to 67% of hospitalized COVID-19 patients. Early evidence indicated that the pathogenesis of COVID-19 evoked ARDS is, at least partially, mediated by hyperinflammatory cytokine storm in which interleukin 6 (IL-6) plays an essential role. The corticosteroid dexamethasone is an effective treatment for severe COVID-19 related ARDS. However, trials of other immunomodulatory therapies, including anti-IL6 agents such as tocilizumab and sarilumab, have shown limited evidence of benefit as monotherapy. But recently published large trials have reported added benefit of tocilizumab in combination with dexamethasone in severe COVID-19 related ARDS. In silico tools can be useful to shed light on the mechanisms evoked by SARS-CoV-2 infection and of the potential therapeutic approaches. Therapeutic performance mapping system (TPMS), based on systems biology and artificial intelligence, integrate available biological, pharmacological and medical knowledge to create mathematical models of the disease. This technology was used to identify the pharmacological mechanism of dexamethasone, with or without tocilizumab, in the management of COVID-19 evoked ARDS. The results showed that while dexamethasone would be addressing a wider range of pathological processes with low intensity, tocilizumab might provide a more direct and intense effect upon the cytokine storm. Based on this in silico study, we conclude that the use of tocilizumab alongside dexamethasone is predicted to induce a synergistic effect in dampening inflammation and subsequent pathological processes, supporting the beneficial effect of the combined therapy in critically ill patients. Future research will allow identifying the ideal subpopulation of patients that would benefit better from this combined treatment.

Bridging the Worlds of Pharmacometrics and Machine Learning.

Precision medicine requires individualized modeling of disease and drug dynamics, with machine learning-based computational techniques gaining increasing popularity. The complexity of either field, however, makes current pharmacological problems opaque to machine learning practitioners, and state-of-the-art machine learning methods inaccessible to pharmacometricians. To help bridge the two worlds, we provide an introduction to current problems and techniques in pharmacometrics that ranges from pharmacokinetic and pharmacodynamic modeling to pharmacometric simulations, model-informed precision dosing, and systems pharmacology, and review some of the machine learning approaches to address them. We hope this would facilitate collaboration between experts, with complementary strengths of principled pharmacometric modeling and flexibility of machine learning leading to synergistic effects in pharmacological applications.

A Real-World Evidence Framework for Optimizing Dosing in All Patients With COVID-19.

Potential treatments for coronavirus disease 2019 (COVID-19) are being investigated at unprecedented speed, and successful treatments will rapidly be used in tens or hundreds of thousands of patients. To ensure safe and effective use in all those patents it is essential also to develop, at unprecedented speed, a means to provide frequently updated, optimal dosing information for all patient subgroups. Success will require immediate collaboration between drug developers, academics, and regulators.

Organs-on-Chips in Clinical Pharmacology: Putting the Patient Into the Center of Treatment Selection and Drug Development.

There have been rapid advances since Organs-on-Chips were first developed. Organ-Chips are now available beyond academic laboratories with the initial emphasis to reduce animal experimentation and improve predictability of drug development through better prediction of safety and efficacy. There is now a huge opportunity to use chips to understand efficacy and disease variability. We propose that by 2030, Organs-on-Chips will play a key role in clinical pharmacology as part of the diagnostic and treatment workflow for some diseases by informing the right drug and dose regimen for each patient.

Model-Informed Artificial Intelligence: Reinforcement Learning for Precision Dosing.

The availability of multidimensional data together with the development of modern techniques for data analysis represent an exceptional opportunity for clinical pharmacology. Data science-defined in this special issue as the novel approaches to the collection, aggregation, and analysis of data-can significantly contribute to characterize drug-response variability at the individual level, thus enabling clinical pharmacology to become a critical contributor to personalized healthcare through precision dosing. We propose a minireview of methodologies for achieving precision dosing with a focus on an artificial intelligence technique called reinforcement learning, which is currently used for individualizing dosing regimen in patients with life-threatening diseases. We highlight the interplay of such techniques with conventional pharmacokinetic/pharmacodynamic approaches and discuss applicability in drug research and early development.

Development and Implementation of Electronic Health Record-Integrated Model-Informed Clinical Decision Support Tools for the Precision Dosing of Drugs.

Imagine it is 2030, and the drug label is in the cloud, is interactive, and can provide model-informed precision dosing support based on an individual's genomic and physiologic makeup that is uploaded via a user-friendly interface. Precision medicine has vastly improved our ability to provide tailored therapeutics, and groundbreaking advances in noninvasive systems have generated smart wearable devices that can follow our physiologic state and drug exposure in real time. One device consists of a microfluidic drug biosensor with a transmitter attached to the skin and a mobile app that displays concentration values and trends while issuing alerts and providing suggestions on dose changes when out of range. This sounds compelling, but why has model-informed precision dosing not yet become common clinical reality in 2020?

Toward Dynamic Prescribing Information: Codevelopment of Companion Model-Informed Precision Dosing Tools in Drug Development.

Model-informed precision dosing (MIPD) is biosimulation in healthcare to predict the drug dose for a given patient based on their individual characteristics that is most likely to improve efficacy and/or lower toxicity compared with traditional dosing. Despite widespread use of biosimulation in drug development, MIPD has not been adopted beyond academic-hospital centers. A reason for this is that MIPD requires more supporting evidence in the language that everyday doctors understand-evidence-based medicine. In this commentary, codevelopment of companion MIPD tools during drug development is advocated as a way to accelerate the generation of the evidence required for broader clinical implementation of MIPD. Such tools have the potential to evolve into "dynamic" prescribing information that could guide dose selection for complex patients.

Driving earlier clinical attrition: if you want to find the needle, burn down the haystack. Considerations for biomarker development.

Drug development attrition rates are rising and late phase attrition remains high, contributing to an unsustainable increase in R&D spending. Consequently, there is much effort to identify the potentially successful molecules earlier in development with the use of biomarkers to predict potential efficacy and safety. However, focussing only on picking the winners earlier will not solve the problem. It is essential that the evaluation of these biomarkers also enables the earlier termination of the molecules that will not have the required activity.

L-5-Hydroxytryptophan augments the neuroendocrine response to a SSRI.

The objective of the study was to assess l-5-hydroxytryptophan's (l-5HTP) augmentation effect on the neuroendocrine response to a SSRI (citalopram). A neuroendocrine challenge study was conducted in healthy Asian male subjects. The neuroendocrine response to oral citalopram and l-5HTP was measured primarily as the prolactin and cortisol area under the response curve (or AUC). The study comprised 2 studies: Study 1. A double blind, randomised dose ranging study was conducted with l-5HTP (50-200 mg) to explore the prolactin and/or cortisol dose response and select a dose that provided a threshold neuroendocrine response. Study 2. A randomized comparison of citalopram 20 vs 40 mg was used to assess the effect of these doses on prolactin and cortisol. Based on the results of the dose response assessments with l-5HTP and cortisol, 200 mg l-5HTP was subsequently used in Study 2 to explore the augmentation of the neuroendocrine response to 20 mg citalopram. Citalopram, but not l-5HTP, increased prolactin AUC(0-3h) while 5HTP and citalopram increased cortisol AUC(0-3h). A 200 mg dose of l-5HTP significantly augmented the prolactin and cortisol response AUC(0-3h) to 20mg oral citalopram. The results of the study suggest that an augmented neuroendocrine challenge may be a suitable marker to demonstrate increased 5-HT-mediated responses when exploring novel agents as improved SSRIs.

The right dose for every patient: a key step for precision medicine.

Understanding the basis of variability in the response of patients to the dose of a drug and a willingness to vary the dose regimen as well as the choice of drug should be one of the key pillars of precision medicine.