Patient-centric clinical pharmacology advances the path to personalized medicine

Embedding Patient-Centricity by Collaborating with Patients to Transform the Rare Disease Ecosystem

What is patient-centricity? In some contexts, it has been associated with targeting therapies based on biomarkers or enabling healthcare access. There has been a surge in patient-centricity publications, and in many cases for the biopharmaceutical industry, patient engagement is used to endorse pre-held assumptions at a specific moment in time. Rarely is patient engagement used to drive business decisions. Here we describe an innovative partnership between Alexion, AstraZeneca Rare Disease and patients that allowed a deeper understanding of the biopharmaceutical stakeholder ecosystem and an empathic understanding of each patient's and caregiver's lived experience. Alexion's decision to build patient-centricity frameworks resulted in the formation of two unique organisation design platforms: STAR (Solutions To Accelerate Results for patients) and LEAP (Learn, Evolve, Activate and deliver for Patients) Immersive Simulations. These interconnected programmes required cultural, global, and organisational shifts. STAR generates global patient insights that are embedded in drug candidate and product strategies while helping to establish enterprise foundational alignment and external stakeholder engagement plans. LEAP Immersive Simulations produce detailed country-level patient and stakeholder insights that contribute to an empathetic understanding of each patient's lived experience, support country medicine launches and provide ideas to have a positive impact along the patient journey. Combined, they deliver integrated, cross-functional insights, patient-centric decision making, an aligned patient journey, and 360° stakeholder activation. Throughout these processes, the patient is empowered to dictate their needs and validate the proposed solutions. This is not a patient engagement survey. This is a partnership where the patient co-authors strategies and solutions.

Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine

Personalized medicine strives to deliver the 'right drug at the right dose' by considering inter-person variability, one of the causes for therapeutic failure in specialized populations of patients. Physiologically-based pharmacokinetic (PBPK) modeling is a key tool in the advancement of personalized medicine to evaluate complex clinical scenarios, making use of physiological information as well as physicochemical data to simulate various physiological states to predict the distribution of pharmacokinetic responses. The increased dependency on PBPK models to address regulatory questions is aligned with the ability of PBPK models to minimize ethical and technical difficulties associated with pharmacokinetic and toxicology experiments for special patient populations. Subpopulation modeling can be achieved through an iterative and integrative approach using an adopt, adapt, develop, assess, amend, and deliver methodology. PBPK modeling has two valuable applications in personalized medicine: (1) determining the importance of certain subpopulations within a distribution of pharmacokinetic responses for a given drug formulation and (2) establishing the formulation design space needed to attain a targeted drug plasma concentration profile. This review article focuses on model development for physiological differences associated with sex (male vs. female), age (pediatric vs. young adults vs. elderly), disease state (healthy vs. unhealthy), and temporal variation (influence of biological rhythms), connecting them to drug product formulation development within the quality by design framework. Although PBPK modeling has come a long way, there is still a lengthy road before it can be fully accepted by pharmacologists, clinicians, and the broader industry.

Genomics, Other “Omic” Technologies, Personalized Medicine, and Additional Biotechnology-Related Techniques

The products resulting for biotechnologies continue to grow at an exponential rate, and the expectations are that an even greater percentage of drug development will be in the area of the biologics. In 2011, worldwide there were over 800 new biotech drugs and treatments in development including 23 antisense, 64 cell therapy, 50 gene therapy, 300 monoclonal antibodies, 78 recombinant proteins, and 298 vaccines (PhRMA 2012). Pharmaceutical biotechnology techniques are at the core of most methodologies used today for drug discovery and development of both biologics and small molecules. While recombinant DNA technology and hybridoma techniques were the major methods utilized in pharmaceutical biotechnology through most of its historical timeline, our ever-widening understanding of human cellular function and disease processes and a wealth of additional and innovative biotechnologies have been, and will continue to be, developed in order to harvest the information found in the human genome. These technological advances will provide a better understanding of the relationship between genetics and biological function, unravel the underlying causes of disease, explore the association of genomic variation and drug response, enhance pharmaceutical research, and fuel the discovery and development of new and novel biopharmaceuticals. These revolutionary technologies and additional biotechnology-related techniques are improving the very competitive and costly process of drug development of new medicinal agents, diagnostics, and medical devices. Some of the technologies and techniques described in this chapter are both well established and commonly used applications of biotechnology producing potential therapeutic products now in development including clinical trials. New techniques are emerging at a rapid and unprecedented pace and their full impact on the future of molecular medicine has yet to be imagined.

Multiple sclerosis: individualized disease susceptibility and therapy response

Multiple sclerosis (MS) is a heterogeneous disease in which diverse genetic, pathological and clinical backgrounds lead to variable therapy response. Accordingly, MS care should be tailored to address disease traits unique to each person. At the core of personalized management is the emergence of new knowledge, enabling optimized treatment and disease-modifying therapies. This overview analyzes the promise of genetic and nongenetic biomarkers in advancing decision-making algorithms to assist diagnosis or in predicting the disease course and therapy response in any given MS patient.