Pharmacogenomics, nutrigenomics, vaccinomics, and the nascent field of plant omics are examples of variability science. They are embedded within an overarching framework of personalized medicine. Across these public health specialties, the significance and biology of the placebo response have been historically neglected. A placebo is any substance such as a sugar pill administered in the guise of medication, but one that does not have pharmacological activity. Placebos do have clinical effects, however, that can be substantive in magnitude and vary markedly from person-to-person depending, for example, on the type of disease, symptoms, or clinical trial design. Research over the past several decades attests to a genuine neurobiological basis for placebo effects. All drugs have placebo components that contribute to their overall treatment effect. Placebos are used in clinical trials as control groups to ascertain the net pharmacological effect of a drug candidate. Not only less well known but also relevant to rational therapeutics and personalized medicine is the nocebo. A nocebo effect occurs when an inert substance is administered in a context that induces negative expectations, worsening patients' symptoms. With the COVID-19 pandemic, there are high public expectations for new vaccines and medicines to end the contagion, while at the same time antiscience, post-truth, and antivaccine movements are worrisomely on the rise. These social movements, changes in public health cultures, and conditioned behavioral responses can trigger both placebo and nocebo effects. Hence, in clinical trials, forecasting and explaining placebo and nocebo variability are more important than ever for robust science and personalized health care. Against this overarching context, this article provides (1) a brief history of placebo and (2) a discussion on biology, mechanisms, and variability of placebo effects, and (3) discusses three emerging new concepts: placebogenomics, nocebogenomics, and augmented placebo, that is, the notion of a "placebo dose." We conclude with a roadmap for placebogenomics, its synergies with the nascent field of social pharmacology, and the ways in which a new taxonomy of drug and placebo variability can be anticipated in the next decade.

Pharmacogenomics, nutrigenomics, vaccinomics, and the nascent field of plant omics are examples of variability science. They are embedded within an overarching framework of personalized medicine. Across these public health specialties, the significance and biology of the placebo response have been historically neglected. A placebo is any substance such as a sugar pill administered in the guise of medication, but one that does not have pharmacological activity. Placebos do have clinical effects, however, that can be substantive in magnitude and vary markedly from person-to-person depending, for example, on the type of disease, symptoms, or clinical trial design. Research over the past several decades attests to a genuine neurobiological basis for placebo effects. All drugs have placebo components that contribute to their overall treatment effect. Placebos are used in clinical trials as control groups to ascertain the net pharmacological effect of a drug candidate. Not only less well known but also relevant to rational therapeutics and personalized medicine is the nocebo. A nocebo effect occurs when an inert substance is administered in a context that induces negative expectations, worsening patients' symptoms. With the COVID-19 pandemic, there are high public expectations for new vaccines and medicines to end the contagion, while at the same time antiscience, post-truth, and antivaccine movements are worrisomely on the rise. These social movements, changes in public health cultures, and conditioned behavioral responses can trigger both placebo and nocebo effects. Hence, in clinical trials, forecasting and explaining placebo and nocebo variability are more important than ever for robust science and personalized health care. Against this overarching context, this article provides (1) a brief history of placebo and (2) a discussion on biology, mechanisms, and variability of placebo effects, and (3) discusses three emerging new concepts: placebogenomics, nocebogenomics, and augmented placebo, that is, the notion of a "placebo dose." We conclude with a roadmap for placebogenomics, its synergies with the nascent field of social pharmacology, and the ways in which a new taxonomy of drug and placebo variability can be anticipated in the next decade.

Precision/personalized medicine is a hot topic in health care. Often presented with the motto "the right drug, for the right patient, at the right dose, and the right time," precision medicine is a theory for rational therapeutics as well as practice to individualize health interventions (e.g., drugs, food, vaccines, medical devices, and exercise programs) using biomarkers. Yet, an alien visitor to planet Earth reading the contemporary textbooks on diagnostics might think precision medicine requires only two biomolecules omnipresent in the literature: nucleic acids (e.g., DNA) and proteins, known as the first and second alphabet of biology, respectively. However, the precision/personalized medicine community has tended to underappreciate the third alphabet of life, the "sugar code" (i.e., the information stored in glycans, glycoproteins, and glycolipids). This article brings together experts in precision/personalized medicine science, pharmacoglycomics, emerging technology governance, cultural studies, contemporary art, and responsible innovation to critically comment on the sociomateriality of the three alphabets of life together. First, the current transformation of targeted therapies with personalized glycomedicine and glycan biomarkers is examined. Next, we discuss the reasons as to why unraveling of the sugar code might have lagged behind the DNA and protein codes. While social scientists have historically noted the importance of constructivism (e.g., how people interpret technology and build their values, hopes, and expectations into emerging technologies), life scientists relied on the material properties of technologies in explaining why some innovations emerge rapidly and are more popular than others. The concept of sociomateriality integrates these two explanations by highlighting the inherent entanglement of the social and the material contributions to knowledge and what is presented to us as reality from everyday laboratory life. Hence, we present a hypothesis based on a sociomaterial conceptual lens: because materiality and synthesis of glycans are not directly driven by a template, and thus more complex and open ended than sequencing of a finite length genome, social construction of expectations from unraveling of the sugar code versus the DNA code might have evolved differently, as being future-uncertain versus future-proof, respectively, thus potentially explaining the "sugar lag" in precision/personalized medicine diagnostics over the past decades. We conclude by introducing systems scientists, physicians, and biotechnology industry to the concept, practice, and value of responsible innovation, while glycomedicine and other emerging biomarker technologies (e.g., metagenomics and pharmacomicrobiomics) transition to applications in health care, ecology, pharmaceutical/diagnostic industries, agriculture, food, and bioengineering, among others.

A joint Canadian Society for Pharmaceutical Sciences and Health Canada workshop entitled "Biowaiver for Immediate and Modified Release Dosage forms" was held in Ottawa, November 19th 2015. A summary of all presentations is included.

OMICS: A Journal of Integrative BiologyVol. 23, No. 3 CommentaryToward Panvigilance for Medicinal Product Regulation: Clinical Trial Design Using Extremely Discordant BiomarkersVural Özdemir and Laszlo EndrenyiVural ÖzdemirAddress correspondence to: Vural Özdemir, MD, PhD, DABCP, Editor-in-Chief E-mail Address: OJIB@liebertpub.comE-mail Address: vural.ozdemir@alumni.utoronto.caOMICS: A Journal of Integrative Biology, New Rochelle, New York.Search for more papers by this author and Laszlo EndrenyiDepartment of Pharmacology and Toxicology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.Search for more papers by this authorPublished Online:18 Mar 2019https://doi.org/10.1089/omi.2019.0013AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetailsCited byTesting the efficacy of tocilizumab in patients with COVID-19 pneumoniaJournal of Comparative Effectiveness Research, Vol. 10, No. 8COVID-19 Is a Potential Bellwether to Inform Future Planetary Health Critical Policymaking Mehmet Ağirbaşli12 March 2021 | OMICS: A Journal of Integrative Biology, Vol. 25, No. 3Genomics, The Internet of Things, Artificial Intelligence, and SocietyPanvigilance: Integrating Biomarkers in Clinical Trials for Systems Pharmacovigilance Semra Şardaş and Aslıgül Kendirci18 March 2019 | OMICS: A Journal of Integrative Biology, Vol. 23, No. 3 Volume 23Issue 3Mar 2019 InformationCopyright 2019, Mary Ann Liebert, Inc., publishersTo cite this article:Vural Özdemir and Laszlo Endrenyi.Toward Panvigilance for Medicinal Product Regulation: Clinical Trial Design Using Extremely Discordant Biomarkers.OMICS: A Journal of Integrative Biology.Mar 2019.131-133.http://doi.org/10.1089/omi.2019.0013Published in Volume: 23 Issue 3: March 18, 2019Online Ahead of Print:February 21, 2019Keywordspanvigilancesystems pharmacovigilanceadverse drug reactionsdrug safetyclinical trial designbiomarkersregulation of medicinal productsPDF download

Aims:In order to ensure the therapeutic equivalence of generic products, it would be important to contrast measures additional to Cmax in order to assess differences in absorption rates. Our aim was to compare partial AUC (PAUC), Swing, and PTF to Cmax in terms of sensitivity, specificity and linearity under identical kinetic conditions. Methods:Single-dose and multiple-dose concentration curves were generated assuming one-compartment models. Kinetic sensitivity curves were obtained by gradually changing the absorption rate constant and keeping all other parameters fixed. Results:A metric should reflect specifically the investigated kinetic feature (e.g., the rate of absorption), be linearly related to it, and should exhibit high kinetic sensitivity. Cmax is related nonlinearly to the rate of absorption, is nonspecific to it (reflects also the extent of absorption as well as the rates of disposition processes), lacks kinetic sensitivity even following a single administration. Compared to Cmax, PAUC was always more sensitive under every investigated condition. Swing and PTF showed high kinetic sensitivity but, in contrast to PAUC, they could be evaluated only in multiple-dose studies. Conclusion:Under identical conditions, different metrics provide widely differing point estimates. Differences in kinetic sensitivity among bioequivalence metrics should be accounted for when results of different metrics are compared.

The Biological Price Competition and Innovation Act (BPCI Act) of 2009 established a pathway for the approval of biosimilars and interchangeable biosimilars in the United States. The Food Drug Administration (FDA) has issued several guidances on the development and assessment of biosimilars which implement the BPCI Act. In particular, a recent draft guidance on the interchangeability of biological products presents an overview of scientific considerations on the demonstration of interchangeability with a reference product. The present communication provides a general summary of the draft guidance and briefly observes a few current issues on interchangeability.

Regulatory authorities introduced procedures in the last decade for evaluating the bioequivalence (BE) for highly variable drugs. These approaches are similar in principle but differ in details. For example, the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) recommend differing regulatory constants. The constant suggested by FDA results in discontinuity of the BE limits around the switching variation at 30% observed within-subject variation of the reference product. The regulatory constant of EMA does not have these problems. The Type I error reaches 6-7% around the switching variation with the EMA constant but 16-17% with the FDA constant. Various procedures were recently suggested, especially for the EMA approach, to eliminate the inflation of the Type I error. Notably, the so-called Exact algorithms try to amalgamate the positive features of both EMA and FDA procedures without their negative sides. The computational procedure for the EMA approach is simple and has a straightforward interpretation. The procedure for the FDA approach is based on an approximation, has a bias at small degrees of freedom, and requires a suitable computer program. All regulatory agencies impose a second requirement constraining the point estimate of the ratio of geometric means. In addition, EMA and Health Canada impose an upper limit for applying the recommended procedures. These expectations have psychological motivation and political rationale but no scientific foundations. Their inclusion results in incorrect and misleading interpretation of the principal criterion which involves confidence intervals. Different regulatory authorities expect to apply their approaches either to both AUC and Cmax or only to AUC or only to Cmax. Rational resolution of the disharmonization is needed.

Approximately one in two patients with a chronic disease does not take their medicines as prescribed. Poor adherence is a worldwide epidemic and a major source of variability in pharmacokinetics (PK) and pharmacodynamics. Without addressing adherence, precision medicine is unlikely to come to fruition. In drug development, poor adherence confounds the estimates for efficacy and safety of drug candidates. Accurate and high-resolution measurement of adherence is a first step toward effective interventions against poor adherence. We describe a new cross-technology platform to measure adherence. The approach involves, first, building PK models to explain dose-exposure relationships. The model incorporates PK biomarkers by genotyping or phenotyping of drug metabolism, transport and other drug clearance pathways. Importantly, dose-exposure data for model building are obtained in healthy volunteer and/or patient cohorts who are ascertained for full adherence, using edible ingestion sensors (IS) that digitize orally administered medicines. Second, the built model is harnessed to back calculate the dose actually ingested by patients, given the empirically observed drug exposure, PK biomarker, demographic, and other patient data. The proposed platform is envisioned to result in development of both drug and drug-specific companion software for adherence measurement. In terms of feasibility, the new approach overlaps with current drug development timelines spanning the Phase 1 to 4 clinical trial continuum, and thus, could conceivably be implemented without requiring significant changes to the time sensitive clinical trial processes. For the IS-powered tools, the proposed platform creates a new space for applications in clinical trials to ensure adherence.

Professor Panos Macheras is a pioneering scientist in pharmacokinetics, pharmacodynamics and biopharmaceutics. His many important contributions to pharmaceutical science are reviewed.

The Global Bioequivalence Harmonization Initiative (GBHI) was launched by the Network on Bioavailability and Biopharmaceutics (BABP) under the auspices of European Federation for Pharmaceutical Sciences (EUFEPS) several years ago. Since 2015, EUFEPS in collaboration with the American Association of Pharmaceutical Scientists (AAPS) has organized three international conferences to support global harmonization of regulatory requirements for bioequivalence (BE) assessment. These conferences provided an open forum for pharmaceutical scientists from academia, industry and regulatory agencies to discuss various BE topics at issue. The current report summarizes the discussion of BE issues at the 2nd GBHI conference held in 2016, Rockville, USA. Three important BE topics were discussed at the meeting: (a) prodrugs and compounds with pre-systemic extraction, (b) scaling procedures and two-stage designs, and (c) exclusion of pharmacokinetic data in BE assessment. The presentations and discussions of these issues have enhanced the mutual understanding of scientific background for BE evaluation and further facilitated harmonization of regulatory approaches for establishing BE of multisource drug products.

OMICS: A Journal of Integrative BiologyVol. 22, No. 8 CommentaryTo Genotype or Phenotype for Drug and Food Safety? Exiting the Technology Echo ChambersVural Özdemir, Laszlo Endrenyi, Nezih Hekim, Tanja Kunej, Lotte M. Steuten, Simon Springer, Semra Şardaş, Erol Ergüler, and Mustafa BayramVural ÖzdemirAddress correspondence to:Vural Özdemir, MD, PhD, DABCPProfessor and Editor-in-ChiefOMICS: A Journal of Integrative BiologyMary Ann Liebert Inc.140 Huguenot StreetNew Rochelle, NY 10801E-mail Address: vural.ozdemir@alumni.utoronto.caEditor-in-Chief, OMICS: A Journal of Integrative Biology, Mary Ann Liebert, Inc., New Rochelle, New York.Search for more papers by this author, Laszlo EndrenyiDepartment of Pharmacology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.Search for more papers by this author, Nezih HekimDepartment of Clinical Biochemistry, School of Medicine, Cumhuriyet University, Sivas, Turkey.Search for more papers by this author, Tanja KunejDepartment of Animal Science, Biotechnical Faculty, University of Ljubljana, Domzale, Slovenia.Search for more papers by this author, Lotte M. SteutenFred Hutchinson Cancer Research Center, Seattle, Washington.Search for more papers by this author, Simon SpringerCritical Geographies Research Laboratory, Department of Geography, University of Victoria, Victoria, Canada.Search for more papers by this author, Semra ŞardaşToxicology Unit, Faculty of Pharmacy, İstinye University, İstanbul, TurkeySearch for more papers by this author, Erol ErgülerIntegrative Medicine Clinic, Nişantaşı, İstanbul, Turkey.Search for more papers by this author, and Mustafa BayramDepartment of Food Engineering, Faculty of Engineering, Gaziantep University, Gaziantep, Turkey.Search for more papers by this authorPublished Online:1 Aug 2018https://doi.org/10.1089/omi.2018.0111AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 22Issue 8Aug 2018 InformationCopyright 2018, Mary Ann Liebert, Inc.To cite this article:Vural Özdemir, Laszlo Endrenyi, Nezih Hekim, Tanja Kunej, Lotte M. Steuten, Simon Springer, Semra Şardaş, Erol Ergüler, and Mustafa Bayram.To Genotype or Phenotype for Drug and Food Safety? Exiting the Technology Echo Chambers.OMICS: A Journal of Integrative Biology.Aug 2018.525-527.http://doi.org/10.1089/omi.2018.0111Published in Volume: 22 Issue 8: August 1, 2018Online Ahead of Print:July 23, 2018Keywordsprecision medicinedrug safetydiagnosticsmulti-omics researchscience governanceinnovation echo chamberpolitical sciencehealth geographyPDF download

The recent book “Biosimilar Clinical Development – Scientific Considerations and New Methodologies” was edited by Kerry B. Barker, Sandeep B. Menon, Ralph B. D’Agostino, Sr., Siyan Xu, and Bo Jin. ...

The determination of the bioequivalence between highly variable drug products involves the evaluation of reference scaled average bioequivalence. The European and US regulatory authorities suggest different algorithms for the implementation of this approach. Both algorithms are based on approximations reflected in lower than the achievable power or higher than the nominal consumer risk of 5%. To overcome these deficiencies, a new class of algorithms, the so-called Exact methods, was earlier introduced. However, their applicability was limited. We propose 2 modifications which make their computation simpler and also applicable with any study design. Four algorithms were evaluated in simulated 3-period and 4-period bioequivalence studies: Hyslop's approach recommended by the US FDA, the method of average bioequivalence with expanding limits requested by the European EMA, and 2 versions of the new Exact methods. At small sample sizes, the Exact methods had substantially higher statistical power than Hyslop's algorithm and had lower consumer risk than the method of average bioequivalence with expanding limits. Similarly to the Hyslop's algorithm, higher than 5% consumer risk was observed only with either unbalanced study design or with additional regulatory requirements. The improved Exact algorithms compare favorably with the alternative procedures. They are based on the bias correction method of Hedges. The recognition that the scaled difference statistics is measured with bias has important practical implications when results of pilot bioequivalence studies are evaluated and, at the same time, calls for the revision of the statistical theory of RSABE and its related methods.

The principal goal of bioequivalence (BE) investigations has crucial importance and has been the subject of extensive discussions. BE studies are frequently considered to serve as procedures for sensitive discrimination. The BE investigation should be able to provide methods and conditions sensitively identifying relevant differences between drug products if such differences in fact exist. Alternatively, BE studies can be deemed as surrogates of clinical investigations assessing therapeutic equivalence. Bioequivalent drug products will be provided to patients for their benefits. Both points of view are valid since they represent two aspects of product performance. It has been argued that both should be equally sustained and applied. In practice, however, they collide when regulatory conditions and statements are developed. For instance, some regulators prefer to conduct BE studies following single drug administrations since these conditions are considered to provide the highest sensitivity of discrimination between pharmacokinetic profiles and thus, a product’s in-vivo performance. Others suggest that, at least for modified-release products, BE investigations should be performed in the steady state since it represents clinical conditions. Preference for one point of view or the other pervades other regulatory statements including suggestions for subjects to be selected in studies and pharmacokinetic measures to be evaluated. An overview is provided on the disturbing inconsistency of statements within and between regulations. It is argued that harmonization would be highly desirable, and relevant recommendations are offered.