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General Topics

Systems Therapeutics Framework: Development and Structure

While a large number of new drugs have been approved by regulatory agencies over the past several decades, and we have witnessed significant scientific advances in molecular understanding of pharmacologic mechanisms and disease processes, there have only been sporadic efforts towards the construction of frameworks for understanding how pharmacologic and pathophysiologic processes interact to produce therapeutic effects. One noteworthy effort was presented in the 1992 edition of Oxford Textbook of Clinical Pharmacology and Drug Therapy (1), which describes the chain of events linking the pharmacologic effects of drugs to their clinical therapeutic response, and includes several examples. For reference, a commonly used generic diagram is shown below illustrating the sequence of events (shown vertically) from a drug dose, through concentration and pharmacologic effect, to therapeutic response.

Efforts on a systems therapeutics diagram were initiated in the mid 2010’s, although it was not initially clear how this work would evolve. The objective was to create a systems therapeutics framework, depicting the pharmacologic and pathophysiologic processes separately (shown horizontally), each with a set of systems components at different biologic levels, thus enabling the presentation of interactions between these two processes at the different biologic levels.

Development of a Systems Therapeutics Framework

The development of the systems therapeutics framework was very much an iterative process, e.g., determining the number and naming of the systems components representing the different biological levels, and their connections and interactions. Five different iterations of a systems therapeutics diagram were subsequently developed and posted on the Therapeutics Research Institute’s website, tri-institute.org, between 2015 and 2018. This involved an evolving construction of a systems therapeutics diagram, four systems therapeutics categories, relevant definitions, and illustrative examples of the different categories, as well as a discussion of variabilities in the pharmacologic and pathophysiologic processes.

During this development period there were important clarifications both at the front end of the diagram, i.e., the drivers of these two processes, pharmacologic agent and intrinsic operator, for the pharmacologic and pathophysiologic processes, respectively, and at the back end, i.e., net therapeutic response, which represents where the clinical (pharmacologic) effect and disease manifestation interact; thus, a symmetry between these processes was achieved. Importantly, this framework shows the pharmacologic processes and the pathophysiologic processes separately, rather than exhibiting a single linear diagram (see diagram above), and thus illustrating for a given pharmacologic agent at what biologic level the pharmacologic and pathophysiologic processes interact to result in a therapeutic response. There was also an ongoing effort on the nomenclature, definitions and examples; these are all provided in Systems Therapeutics: Diagram, Definition and Illustrative Examples, Therapeutics Research Institute’s website (2).

Systems Therapeutics Diagram

Systems therapeutics defines where pharmacologic processes and pathophysiologic processes interact to produce a clinical therapeutic response (see diagram below).

The organizing principle underlying the systems therapeutics diagram involves two rows of four parallel systems components for pharmacologic and pathophysiologic processes, representing the four different biologic levels of interactions between these two processes, i.e., at the molecular level, the cellular level, the tissue/organ levels, and finally the clinical level, in addition to the initiating entities or drivers of these processes, and the ultimate therapeutic response.

The systems components for pharmacologic processes involve a pharmacologic response element, followed by a pharmacologic mechanism, a pharmacologic response, and a clinical effect, whereas the systems components for pathophysiologic processes involve an etiologic causative factor, followed by a pathogenic pathway, a pathophysiologic process, and a disease manifestation. The four different biologic levels of interactions between these two processes then determine the four systems therapeutics categories, i.e., Category I (at the molecular level), Category II (at the cellular level), Category III (at the tissue/organ level), and Category IV (at the clinical level).Both of these two processes are initiated by their own sets of initiators or drivers, i.e., a pharmacologic agent and an intrinsic operator, for the pharmacologic and pathophysiologic processes, respectively. On the pharmacologic process side, a pharmacologic agent (a drug), interacting with a pharmacologic response element (a receptor or so-called drug target), and its concentration or exposure, is the fundamental driver of pharmacologic processes. On the pathophysiologic process side, a hypothetical intrinsic operator is proposed as an initiator interacting with and influencing an etiologic causative factor, and serving as a driver of pathophysiologic processes. This hypothetical intrinsic operator is intended to cover biologic entities identified using advanced network-based approaches in disease initiation.

The culminating result of the interaction between these two processes, independent of the biologic level of the pivotal interaction, involves a clinical therapeutic response, determined by the clinical (pharmacologic) effect and disease manifestation. While it is well recognized that there is a wide variability in the clinical therapeutic response of individual patients to a given approved drug, it is less well recognized that both of these two processes, pharmacologic and pathophysiologic, have their inherent variabilities. This systems therapeutics construct thus further suggests that interpatient variabilities in both of these active processes contribute to and thus are co-determinants of the ultimate patient therapeutic response characteristics, including range and extent of response, response variability, and responder rate. Presently, however, the relative contributions of each of these process variabilities to the ultimate therapeutic response are typically unclear, most significantly due to limited availability of data and methods, and are likely to vary from one therapeutic class to another.

Systems Therapeutics Categories

The systems therapeutics diagram lends itself to determine four systems therapeutics categories, corresponding to the four different biologic levels of interactions between pharmacologic processes and pathophysiologic processes, as follows:

Category I – Molecular Level: Elements/Factors

Definition – The pivotal interaction between pharmacologic processes and pathophysiologic processes involves the primary corresponding molecular entities, the pharmacologic response element and the etiologic causative factor, respectively.

Examples of Molecular-based Therapy – These can involve replacement therapies (hormones, enzymes, proteins, genes) or genome-based therapies (interference with altered gene products).

Category II – Cellular Level: Mechanisms/Pathways

Definition – The pivotal interaction between pharmacologic processes and pathophysiologic processes involves a fundamental biochemical mechanism, related to the disease evolution, although not necessarily an etiologic pathway.

Examples of Metabolism-based Therapy – These can involve metabolism-based therapies (interference with a biochemical mechanism or a disease network-linked pathway).

Category III – Tissue/Organ Level: Responses/Processes

Definition – The pivotal interaction between pharmacologic processes and pathophysiologic processes involves a modulation of a (normal) physiologic function, linked to the disease evolution, although not necessarily an etiologic pathway.

Examples of Function-based Therapy – These can involve function-based therapies (modulation of a (normal) physiologic function or activity).

Category IV – Clinical Level: Effects/Manifestations

Definition – The pivotal interaction between pharmacologic processes and pathophysiologic processes involves an effect directed at clinical symptom(s) of a disease, but not directly its cause or etiology.

Examples of Symptom-based Therapy – These can involve symptom-based therapies (various symptomatic or palliative treatments).

Examples, Definitions, and Glossary

In addition to descriptions of the systems therapeutics framework and diagram, examples of approved drugs for each of the systems therapeutics categories were provided in Systems Therapeutics: Diagram, Definitions and Illustrative Examples, which was posted on the Therapeutics Research Institute’s website (2). Furthermore, this post also contains illustrative examples for the different systems therapeutics categories of how the pivotal interaction occurs between the two processes. Finally, this post also contains definitions and a glossary of the individual systems components for the pharmacologic and pathophysiologic processes represented in the systems therapeutics diagram.

Discussion

The systems therapeutics diagram and framework presented here represents the culmination of years long effort on the pharmacotherapeutics process. Of note are the two initiators or drivers of these two processes, one actual (pharmacologic agent), the other hypothetical (intrinsic operator); the four systems components for each of the pharmacologic and pathophysiologic processes and their interactions to create four different systems therapeutic categories; and the final common therapeutic response from the two end events of the pharmacologic and pathophysiologic processes, clinical effect and disease manifestation.

It is worth highlighting a few aspects of the systems therapeutics framework and contrast these to the “classic” single linear diagram from a dose of a drug to pharmacologic effect or therapeutic response:

They differ in structure: The systems therapeutics framework involves two parallel processes, pharmacologic and pathophysiologic, each showing four biologic levels of potential interactions, at the molecular, cellular, tissue/organ and clinical levels, initiated by their respective drivers and culminating in a therapeutic response, in contrast to the single linear model from drug dose and exposure through pharmacologic actions, e.g., molecular, cellular, tissue/organ or clinical levels, resulting in pharmacologic effect or therapeutic response.
They differ in suggestive causes of variabilities in therapeutic response: The systems therapeutics framework recognizes the potential for interindividual variability not only on the pharmacologic process side, e.g., due to exposure or pharmacologic receptor differences, but also on the pathophysiologic side, e.g., due to differences in pathogenic pathways or disease manifestations, whereas the single linear sequence model views drug exposure as the key determinant of variability in pharmacologic effect or therapeutic response. Thus, the systems therapeutics framework views variabilities in both processes as contributors and co-determinants of variability in therapeutic response, although at the present time it may be difficult to determine pathophysiologic process variability and the respective contributions of both processes.
They differ in their abilities to illustrate how the pharmacologic and pathophysiologic process components interact: The systems therapeutics framework allows for a mechanistic explanation how the two processes interact, depending on the biologic level of the pivotal interaction. This has been illustrated by examples for each of the four levels of interactions, i.e., systems therapeutics categories, how the pharmacologic and pathophysiologic processes interact to produce a therapeutic response. There is not an equivalent manner to illustrate these interactions in the single linear sequence model.
They differ in their focus on the initiators or drivers of their respective processes: The systems therapeutics framework allows for initiators or drivers of both processes. On the pharmacologic process side, the fundamental driver of the pharmacologic processes obviously is the pharmacologic agent in question and its exposure, whereas, on the pathophysiologic process side, a hypothetical intrinsic operator is proposed as an initiator interacting with and influencing an etiologic factor, and thus serving as a driver of the pathophysiologic processes. While currently there are no known examples of such initiators or drivers, current work in advanced network-based approaches in disease initiation suggests the existence of a driver mechanism. There is not an equivalent manner to suggest a disease process driver in the single linear sequence model.

Considering the above highlights, the systems therapeutics framework suggests future research needs, such as to address disease process measurements and variability, and to further define disease process initiators and drivers. It is noted that the some of the overall variability in conventional PK-PD modeling is likely to be due to variability in disease processes.

It is hoped that the systems therapeutics framework advanced here will help stimulate research towards qualitative and quantitative descriptions of the pharmacologic and pathophysiologic processes, including ways of defining the relative contributions of these two processes towards determining the overall therapeutic response characteristics.

References

Grahame-Smith DG, Aronson JK: Oxford Textbook of Clinical Pharmacology and Drug Therapy, Oxford University Press, Oxford, 1992 (Chapter 5. The Therapeutic Process, pp. 55-66).
Bjornsson, TD: Systems Therapeutics: Diagram, Definitions and Illustrative Examples. Therapeutics Research Institute’s website, tri-institute.org, April 2018, last edited August 5, 2021.

Pharmaceutical Predictivity

Pharmaceutical Attrition

Drug development is associated with a significant degree of attrition, which is generally thought to be in the 90 percent range, indicating that only about 10 percent of compounds entering development make it to regulatory submission, approval and market. During the past two decades numerous papers have been published on pharmaceutical attrition, attempting to arrive at an overall estimate of attrition, and the key reasons for attrition (e.g., Kola and Landis, 2004; Hay et al., 2014). Attrition rates have been reported to vary significantly depending on different attributes, such as therapeutic areas, phase of development and pharmacologic targets, and commonly cited causes of compound termination have included clinical safety, lack of efficacy, formulation, PK/bioavailability, commercial, toxicology, and cost of goods.

A general comment concerning the published literature on attrition and its causes, as well as assessments within individual pharmaceutical companies, is that definitions and attributions have not been standardized, and common methodology has been lacking. It is also noted that even within individual companies, when reviewing their compound termination databases, it’s not always clear what was considered the primary reason for the termination of a specific compound by the project team. Furthermore, the literature focuses primarily on small molecules, but success rates of biopharmaceuticals and vaccines are thought to be considerably better than those for small molecules.

Considering the significant negative implications of candidate compound attrition for the pharmaceutical industry, principally in terms of R&D expenditure, opportunity cost and productivity, the question arises how the industry might be able to reduce attrition, and thus be able to bring more new therapies to market. As an example, an R&D organization might base how many candidate compounds to bring into development each year on the desired number of drug candidates to be submitted for regulatory submission at a future date following clinical development. Thus, assuming a goal of one regulatory submission per year, at least ten candidate compounds would need to be brought into development each year. Such an inefficient drug development process obviously makes it much harder to address the many unmet medical needs areas.

The purpose of this commentary is to present a framework to address attrition of small molecules from a different perspective. It is not intended in any way to provide an extensive literature review of this complex topic; references are representative.

From Attrition to Predictivity

While different high-level categories have been proposed as reasons for attrition, such as scientific reasons, technical reasons, commercial reasons and regulatory reasons, each with their specific underlying set of causes, fundamentally the key causes of terminations are scientific, i.e., based on lack of efficacy, safety issues, and undesirable compound properties (compound properties are defined as determinants and descriptors of drug exposure). These are also the causes of terminations that together are by far the most common, and that lend themselves to addressing attrition by better understanding of the underlying scientific causes. This involves focusing on predictivity, i.e., how predictive are the preclinical methods and models of clinical performance, as expressed below:

P_T = p_e x p_s x p_c

Where P_Tstands for total predictivity with respect to scientific causes of terminations, and p_e, p_s and p_c stand for the individual predictivities of efficacy, safety and compound properties, respectively. This is shown graphically below, and in an enlarged format here.

Note that the predictivities of these three scientific components are considered to be multiplicative, which assumes these are independent of each other. Also note that this leaves aside terminations by attrition categories such as cost of goods, formulation issues, budget/resource constraints, portfolio rationalization, potential values, patent issues and regulatory hurdles, as these are fundamentally unrelated to the three scientific causes mentioned above, and are typically business-related rather than scientific.

Predictivity Rates of Discovery and Preclinical Data

As was mentioned above, the data and methodologies that have been used to address attrition and attrition categories, as reflected in the published literature, have generally not been uniform or standardized. Yet in order to identify opportunities to reduce attrition, what’s needed are well-defined studies with robust statistical analyses of how predictive current preclinical studies of efficacy, safety and compound properties are with respect to clinical performance. With an overall success rate of only about 10%, for small molecules at least, one has to conclude that our understanding of how to confidently progress candidate compounds from preclinical to clinical is limited. Indeed, to quote Sir Peter Medawar, Nobel Laureate in Physiology and Medicine 1960: “No branch of science can be called truly mature until it has developed some form of predictive capability.”

A table of examples of preclinical methods and models of efficacy, safety and compound properties and examples of clinical failures due to lack of efficacy failures, safety/toxicology issues or undesirable compound properties is shown below, and in an enlarged format.

At the present time, specific values for predictivities of efficacy, safety and compound properties are not readily available in the publishedliterature, but it is possible to arrive at a general range for these, at least for compound properties and safety. While there are obviously a variety of complicating factors, such as the chemical properties of the candidate compounds, which therapeutic areas, and which specific parameters are being addressed for these scientific causes of termination, such guesstimates can be useful for identifying where the weaknesses in the current preclinical-to-clinical paradigm lie.

Compound Properties. A number of papers have been published on the predictivity of compound properties based on preclinical data and using a variety of computational models to predict PK parameters in humans and compared these with the observed parameters in man, typically from early Phase I studies (e.g., PISC/PhRMA group, 2011; Jones et al., 2013). The parameters addressed have included PK parameters, such as total clearance, AUC, maximum concentration, elimination half-life, bioavailability and food effect. The computational methods have included allometry, in vitro-in vivo extrapolation (IVIVE) and physiologically-based pharmacokinetics (PBPK). The published reports have included data from individual companies and from multi-company precompetitive collaborative efforts. In general, the predictivity for the individual compound properties parameters have broadly ranged from about 0.4 to 0.7 (predictions within a factor of two), although there has been considerable variability from report to report; PBPK appears to be the favored method.

Safety. There have also been numerous published studies addressing predictivity of safety, typically based on the concordance between adverse effects in man (after Phase I or after clinical development) and the findings from preclinical safety studies (e.g., Olson et al., 2000; Monticello et al., 2017). In general, these studies have found good concordance between the findings in preclinical and clinical studies, although there has typically been considerable variability with respect to different organ systems. While methodologies based on concordance are not specifically addressing predictivities, these are generally thought to be of a similar magnitude as those for compound properties, in the broad range of 0.4 to 0.7. It is noteworthy that regulatory requirements for preclinical studies for safety and compound properties are well standardized and prescriptive, including ICH guidelines.

Efficacy. In stark contrast to predictivity of safety and compound properties, the situation with respect to how predictive preclinical efficacy studies and methods are with respect to efficacy in humans is significantly more limited. Numerous challenges in this area have been suggested, such as non-uniform preclinical studies and methodologies associated with preclinical animal models, small sample sizes and translational challenges. However, for illustrative purposes in this commentary, one can grossly estimate these to be around 0.3, by solving for total predictivity of 0.10 and using 0.6 values for both predictivities of safety and compound properties; using values of 0.5 and 0.7 for safety and compound properties, efficacy predictivity values would be in the 0.4 and 0.2 range, respectively. Of the three scientific causes of attrition, clearly predictivity of efficacy represents the weakest link in successful progression of candidate compounds from the preclinical to the clinical stage of development.

Conclusion

The present commentary focuses on scientific causes of attrition, and the predictivities of those: efficacy, safety and compound properties. Non-scientific causes of attrition, as reported in the published literature, have varied somewhat, in percentage of total attrition and being generally business-related. Since these are not included, it follows that the above predictivity estimates are likely to be somewhat undervalued.

These considerations suggest that in order to meaningfully reduce attrition rate, much more work is needed to better define the predictivities of preclinical methods and models, particularly those with respect to efficacy. Work in this general area to date has clearly shown the value of precompetitive collaboration across the industry, as exemplified by recent work on compound properties (e.g., PISC/PhRMA group) and on safety (e.g., DruSafe group). It is important to note however that such collaborative work is both resource and time intensive and requires long-term commitment by all the concerned participants. Yet, it is hard to imagine how pharmaceutical productivity can be improved without it.

Predictivity => Productivity

References

Hay M, Thomas DW, Craighead JL, et al. Clinical development success rates for investigational drugs. Nat. Biotechnol., 32: 40-51, 2014.

Jones HM, Mayawala K, Poulin P. Dose selection based on physiologically based pharmacokinetic (PBPK) approaches. The AAPS J. 15(2): 377-387, 2013

Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov., 3:711-716, 2004.

Monticello TM, Jones TW, Dambach DM, et al. Current nonclinical testing paradigm enables safe entry to First-in-Human clinical trials: The IQ consortium nonclinical to clinical translational database. Toxicol. Appl. Pharmacol., 334: 100-109, 2017.

Olsen H, Betton G, Robinson D, et al. Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul. Toxicol. Pharmacol., 32(1): 56-67, 2000.

PISC/PhRMA group. J. Pharmaceut. Sci, 100(10): 4045-4157, 2011. This issue has five papers from a PhRMA working group on predicting human pharmacokinetics, plus an editorial and a commentary by its two consultants.

On Naming Categories

Personal Experience and Observations

Sometimes when you are writing a paper or a report, and you have gotten the main things down – the text and the figures – there is this one issue you need to finalize: the naming of categories included in the paper. Although this is an integral part of the paper, you just don’t consider it as a deciding factor for the acceptance of the work for publication, more like an embellishment. As an example, this could involve work on a categorization of a biomedical system, and you have already decided on the number of categories and their definitions. But what to name the categories?

Writing the Draft with Categories Named by Descriptive Terms

When I was writing the draft of a paper on a categorization of drug treatments based on the relationship between the therapeutic effect of a drug and the pathophysiologic process being treated or prevented (i.e., therapeutic specificity) – this was in 1995 and I was on the faculty at an academic medical center – and after I had completed the text and defined six therapeutic specificity categories, the question arose what their names should be. I recall spending quite some time on what to name the categories, and finally I came up with six descriptive names, using the adjectives preventing, inhibiting, intervening, altering, improving and facilitating. Feeling pretty good about the draft, I submitted the manuscript to Clinical Pharmacology & Therapeutics, but the manuscript was rejected and not considered for publication.

Finalizing the Paper with Categories Named by Roman Numerals

I told one of my senior medical school administrators about my experience, and he suggested I talk to a professor at the school who had been an editor of a journal in his discipline for a long time. So I sent him the manuscript for comments, and within a day or two he called me back and told me that in his opinion the paper read and looked fine, but I should not use descriptive names for categories of therapeutic specificity, because reviewers – and readers – will all get hung up on descriptive terms. So, that’s what I did, I just changed the names from descriptive names to numbered items, using Roman numerals (plus “0”), based on the relationship between the therapeutic effect of a drug and the pathophysiologic process(es) being treated or prevented, as follows:

Category 0: for disease prevention
Category I: directed at disease etiology
Category II: directed at specific disease processes
Category III: directed at specific disease manifestations
Category IV: for non-specific disease manifestations
Category V: for non-therapeutic drug use

An astute reader will note that if we don’t consider Categories 0 and V, which involve preventative treatments like vaccines and drugs like anesthetics, respectively, then the publication basically involves four categories of drug treatments for specific diseases. I the submitted the manuscript to the Journal of Clinical Pharmacology, which promptly accepted it, and that’s where was published: A Classification of Drug Action Based on Therapeutic Effects. J. Clin. Pharmacol., 36(8):669-673, 1996 (for therapeutic specificity scheme refer to Figure 2).

There are of course other non-descriptive approaches to naming categories that I could have used, such as numbers or letters, but I settled on Roman numerals as that felt less ordered than plain numbers, these categories being more categorical than hierarchical. In more complicated situations where there might be categories and subcategories, other approaches can be used, such as alphanumeric approaches, and those including lower-case and upper-case letters.

Subsequent Evolution of The Therapeutics Categorization Project

What happened next? More than two decades later – this was in 2018 and I had retired from the pharmaceutical industry – this general therapeutics categorization scheme had evolved into a much more comprehensive description of systems therapeutics, addressing how the pharmacologic and pathophysiologic processes interact, to culminate in a therapeutic response. This is based on a systems therapeutics diagram, which consists of two rows of parallel systems components for pharmacologic and pathophysiologic processes, representing four different biologic levels of interactions between these two processes, i.e., at the molecular, cellular, tissue/organ or the clinical levels, as follows:

Category I: at the molecular level, involving elements/factors
Category II: at the cellular level, involving mechanisms/pathways
Category III: at the tissue/organ level, involving responses/processes
Category IV: at the clinical level, involving effects/manifestations

This latter work, Systems Therapeutics: Diagram, Definitions and Illustrative Examples, was posted on the Therapeutics Research Institute’s website (tri-institute.org), in April, 2018 (for systems therapeutics diagram refer to diagram on page 2).

Takeaways

What are the takeaways from this experience? First, when naming categories avoid using descriptive terms, instead use numbers/numerals or letters. Second, regarding the number of categories, observe the parsimony principle, use only as many as are absolutely needed; three would typically be too few, and often four or five will work fine. Third, conceptual projects may often require many iterations and years to evolve as new experience and knowledge has accumulated to inform the work. Fourth, and not the least, seek help and advice from your colleagues and network; the systems therapeutics project might not have happened if the original therapeutic specificity project had not gotten published.

This post has also been published in Medium in August 2020, under the same title.

Infographics of Modern Therapeutics

Introduction

The purpose of this report is to provide highlights from two posts involving infographics of modern therapeutics, since these posts were originally only listed under their months of posting, but not indicating their titles. These were as follows:

Great Variability in New Drug Approvals Among Pharmacologic Classes and Therapeutic Classes, posted February 2016, and
Snapshots from Progression of Modern Therapeutics, posted June 2016.

The graphs in these two posts are in addition to extensive infographics on the progression of 40 therapeutic classes, across 14 therapeutic categories, included in Progression of Modern Therapeutics, issued January 2016, and in a more extended reporting on 16 therapeutic classes, summarized in List of 16 Posts on Individual Therapeutic Classes, issued January 2018.

Methodology

The data used in both of these two above referenced posts are originally from Progression of Modern Therapeutics, which covers 40 therapeutic classes from 14 therapeutic categories, and which includes a detailed description of the methodology and definitions used in this project. Note the following definitions used throughout:

Modern therapeutics – refers to those new drug approvals belonging to a given pharmacologic class that were first approved in the 1970’s to 1980’s timeframe and going forward, as further defined in Progression of Modern Therapeutics.
Pharmacologic class – refers typically to a biologic target-based or mechanism of action-related classification, but in some instances involves a chemical classification, or a mix of the two.
Therapeutic class – refers to new drug approvals for a given disease or indication, independent of pharmacologic class.
Therapeutic category – refers to approved therapeutics in a given anatomical organ or system.
Length of registration interest – refers to the time interval between the dates of the first and the latest new drug approval within a given pharmacologic class.

Number of Pharmacologic Classes per Therapeutic Classes

The number of pharmacologic classes per individual therapeutic classes for new drug approvals is shown in the graph below, in a descending order, for 38 of the 40 therapeutic classes covered in Progression of Modern Therapeutics.

Click here for a larger graph. Note the wide range in the number of pharmacologic classes per therapeutic classes, ranging from 11 and 9 for Type-2 Diabetes and Multiple Sclerosis, respectively, and 8 each for Rheumatoid Arthritis and Melanoma, to 1 each for Idiopathic Thrombocytopenic Purpura and Systemic Lupus Erythematosus, and several therapeutic classes with 2 each, including Alzheimer’s Disease and Schizophrenia. Note that the mean and median for the number of pharmacologic classes per individual therapeutic classes (N=38) are 4.3 and 4.0, respectively.

New Drug Approvals per Therapeutic Classes

The number of new drug approvals for 38 of the 40 therapeutic classes covered in Progression of Modern Therapeutics is shown in the graph below, in descending order.

Click here for a larger graph. Note the wide variability in the number of new drug approvals for the different therapeutic classes, ranging from 44 for Hypertension, 28 for HIV-1/AIDS, 27 for Type-2 Diabetes and 23 for Schizophrenia, to 1 for Systemic Lupus Erythematosus, 2 each for Idiopathic Thrombocytopenic Purpura and Idiopathic Pulmonary Fibrosis (both orphan indications) and 3 for Fibromyalgia. Note that the mean and median for the number of new drug approvals per individual therapeutic classes (N=38) are 11.5 and 9.5, respectively.

Selected Snapshots from Noteworthy Patterns in Registration Avivities

Below are two selected snapshots based on data in Progression of Modern Therapeutics, each illustrating a specific pattern in registration activities of modern therapeutics:

Snapshot #1: Two therapeutic classes showing two key pharmacologic classes with no overlaps in registration activities, i.e., H2 Receptor Antagonists and Proton Pump Inhibitors for Acid Reflux and Ulcer Disease, and Benzodiazepines and Non-benzodiazepines for Insomnia. Both examples show an abrupt switch in new drug introductions from older pharmacologic classes to newer pharmacologic classes. For a larger graph click here.

Snapshot #2: Two therapeutic classes showing two key pharmacologic classes with overlaps in registration activities, i.e., Corticosteroids and Beta-2 Adrenergic Agonists for Asthma, and SSRI’s and SNRI’s for Depression. Both examples show concurrent new drug introductions for two dominant or somewhat similar pharmacologic classes or mechanisms of action. For a larger graph click here.

Length of Registration Interest

Perusal of the graphs for 40 therapeutic classes in Progression of Modern Therapeutics also illustrates a wide variability in the length of registration interest, i.e., the time interval between the dates of the first and the latest new drug approval within a given pharmacologic class (shown on the right hand side of the graphs). Of the more than 180 pharmacologic classes covered, it is if interest to note that 16 classes have lengths of registration interest longer than 25 years, e.g., Beta-Blockers for Hypertension (40.1 decimal years) and Typical and Atypical Antipsychotics for Schizophrenia (26.9 and 25.9 decimal years, respectively), and that lengths between 10 and 20 years are quite common for established pharmacologic classes, e.g., TNF Inhibitors for Rheumatoid Arthritis (10.5 decimal years) and PDE-5 Inhibitors for Erectile Dysfunction (14.1 decimal years).

Comments

The infographics of modern therapeutics presented in this report involve examples from two previous posts, but are highlighted here, since the original posts were only listed under their months of posting (February 2016 and June 2016), but not indicating their titles.

The graphs and texts illustrate wide variability in new drug approvals among the different pharmacologic classes and therapeutic classes. This involves both the number of pharmacologic classes per individual therapeutic classes, and the number of new drug approvals per individual therapeutic classes. For example, the number of pharmacologic classes per individual therapeutic classes ranged from 11 (Type-2 Diabetes) to 1 (Systemic Lupus Erythematosus), and the number of new drug approvals per individual therapeutic classes ranged from 44 (Hypertension) to 1 (Systemic Lupus Erythematosus). Also noteworthy is that 16 pharmacologic classes of the more than 180 covered have lengths of registration interest longer than a quarter of a century; also note the most common interval of lengths of registration interest is 10-20 years (31 pharmacologic classes), for lengths longer than 5 years.

Note these graphs only involve new drug approvals, and do not include generics, new formulations, or new trademarks of previously approved chemical entities. Also note these graphs and texts are based on data available in early 2016; there may have been a few new drug approvals in some of the covered therapeutic classes since then, but not so much as to alter the key conclusions presented.

It is tempting to speculate what might be the reasons underlying such wide variabilities in the number of pharmacologic classes per therapeutic classes, in the total number of new drug approvals per therapeutic classes, and in the relatively long periods of registration interest for numerous pharmacologic classes, but that will be left to another time. Public discussion on these important topics, however, is very important, since at a high level, these are likely to relate to how society in general – including the academic research community, the regulatory agencies, the pharmaceutical R&D community, and patient and disease organizations – attempt to address varying degrees of scientific knowledge about disease etiology and pathophysiology, levels of research funding, commercial assessment, and different levels of unmet medical need.

List of 16 Posts on Progression of Individual Therapeutic Classes

Introduction

Of the 40 therapeutic classes (across 14 therapeutic categories) that have examined Progression of Modern Therapeutics (last report issued in early 2016, under Reports) a more extended reporting has been made on a total of 16 therapeutic classes, under Posts. The majority of these posts involved the following subtitles: Background, Drug Approvals, Comments, and References (as appropriate), as exemplified by the post on Benign Prostatic Hyperplasia (BPH: Relying on Mechanisms First Approved a Quarter of a Century Ago, August 2016, click here), in addition to a timeline chart illustrating the individual approved drugs and their mechanisms of action.

Since these posts on individual therapeutic classes were only listed under their months of posting, the purpose of this reporting is to summarize 16 of these therapeutic classes under their therapeutic categories, including the titles of these posts.

Cardiovascular Therapeutics
Hypertension Progression of Modern Antihypertensive Therapeutics: An Example of a Mature Therapeutic Class. (December 2014) Click here.
Dyslipidemia Lipid Lowering Drugs: Steady Progress Over Half a Century. (September 2015) Click here.

Hematologic Therapeutics
Thrombosis Progression of Modern Antithrombotic Drugs: What’s Next? (January 2015) Click here.

Gastroenterologic Therapeutics
Acid Reflux and Ulcer Disease Progression of Acid Reflux and Ulcer Disease Therapeutics. (October 2014) Click here.
Irritable Bowel Syndrome IBS: Underlying Mechanism(s) Not Established but Six New Drug Approvals Since 2000. (September 2016) Click here.

Endocrinologic Therapeutics
Type-2 Diabetes Progression of Modern Therapeutics for Type-2 Diabetes. (November 2014) Click here.
Obesity Weight Reduction Drugs: Slow Progression and Innovation. (March 2015) Click here.
Osteoporosis Osteoporosis: Better Drugs and Better Scientific Understanding Needed. (July 2016) Click here.

Psychopharmacologic Therapeutics
Schizophrenia and Depression Slow Progress in Psychopharmacologic Therapeutics. (September 2014) Click here.
Attention Deficit Hyperactivity Disorder ADHD Therapeutics: Slow and Limited Progress. (November 2015) Click here.
Insomnia Modern Insomnia Therapeutics: Not the Barbiturates or Anxiolytic Benzodiazepines of Yore. (April 2016) Click here.

Neurologic Therapeutics
Alzheimer’s Disease Alzheimer’s Disease: Many Years to Go. (June 2015) Click here.
Migraine Migraine Therapeutics: Slow Progress Towards Precision Medicine. (December 2015) Click here.

Rheumatologic Therapeutics
Rheumatoid Arthritis Progression of Modern Therapeutics for Rheumatoid Arthritis. (October 2015) Click here.
Gout and Hyperuricemia Gout and Hyperuricemia: Reliance on Old Mechanisms. (March 2016) Click here.

Genitourinary Therapeutics
Benign Prostatic Hyperplasia BPH: Relying on Mechanisms First Approved a Quarter of a Century Ago. (August 2016) Click here.

2016 Update on Current Projects

Introduction

The Therapeutics Research Institute, a 501(c)(3) nonprofit corporation located in Saint Davids Pennsylvania, was initiated in August 2012 to examine and report on various aspects of modern therapeutics. Its website, TRI-institute.org, which contains all its reports and commentaries, became operational in August 2014.

Objectives

The objectives of the Therapeutics Research Institute (TRI-institute), which are listed at TRI-institute.org/Organization, are:

To conduct scientific assessments of characteristics of drug treatments of human diseases based on available information and relevant frameworks;
To analyze and report such findings by indications and therapeutic areas, pharmacological mechanisms, types of endpoints, and disease types;
To co-sponsor seminars, particularly in the Greater Philadelphia region, directed at the pharmaceutical startup community, exploring lessons from the findings; and
To engage in other activities related to the objectives of the corporation, that will further its mission.

Current Projects

High-level outlines of the three current projects, Progression of Modern Therapeutics, Characteristics of Therapeutic Response, and Systems Therapeutics, are shown on the graph below (click here for a larger graph).

Update on Current Projects

A summary Update on Current Projects, for the three current projects, are shown on the graph below (click here for a larger graph). The dates of individual reports and posts (month, year) are also shown, except those for the 17 commentaries on individual therapeutic classes.

Examples from Recent Work

Below are three examples from recent work, i.e., from one of the 17 commentaries on individual therapeutic classes, from an analysis of the number pf pharmacologic classes per therapeutic classes and the number of approved new molecules per pharmacologic classes, and on an updated diagram on systems therapeutis.

New Drug Molecules for Osteoporosis

Above is a chart from one of the 17 posted commentaries on individual therapeutic classes, which had originally been included in Progression of Modern Therapeutics (2015 Report), this one on approved modern therapeutics for osteoporosis, from Osteoporosis: Better Drugs and Better Scientific Understanding Needed (July 2016). These commentaries have included, to name a few, Irritable Bowel Syndrome (September 2016), Benign Prostatic Hyperplasia (August 2016), Migraine (December 2015), Rheumatoid Arthritis (October 2015) and Lipid Lowering Drugs (September 2015).

Regarding osteoporosis, since 1984, a total of 9 new drug molecules have been approved for osteoporosis, in 5 pharmacologic classes. The 1995 approval of Fosamax (alendronate), the first of 4 approved bisphosphonates, represented an important milestone for this therapeutic class. The other 4 pharmacologic classes involve the SERM’s (Selective Estrogen Receptor Modulators), calcitonins, parathyroid analogs, and RANK (Receptor Activator of Nuclear factor Kappa-B) ligand. It was also commented on concerns about bone quality and the effects current osteoporosis drugs may have on bone quality, and that a new FDA draft guidance called for additional long-term nonclinical pharmacology studies (bone quality studies) to support new osteoporosis drug development.

Number of Pharmacologic Classes per Therapeutic Classes

The above chart shows the number of modern therapeutics’ pharmacologic classes per 38 of the 40 therapeutic classes covered in Progression of Modern Therapeutics (2015 Report), from Great Variability in New Drug Approvals Among Pharmacologic Classes and Therapeutic Classes (February 2016). This graph illustrates the significant variability in the number of pharmacologic classes per therapeutic classes, ranging from as high as 11 for Type-2 Diabetes to 1 for Systemic Lupus Erythematosus and Idiopathic Thrombocytopenic Purpura. Note the mean and median values for the number of pharmacologic classes per therapeutic classes were 4.3 and 4, respectively. The same commentary also showed a wide range in the total number of new drug approvals for these therapeutic classes, ranging from 44 for Hypertension to 1 for Systemic Lupus Erythematosus; note he mean and median values for the number of new drug approvals for these therapeutic classes were 11.5 and 9.5, respectively.

Systems Therapeutics

Above is the recently updated systems therapeutics diagram, from Systems Therapeutics: Updated Diagram (October 2016). The systems therapeutics diagram consists of two rows of four parallel systems components for pharmacologic and pathophysiologic processes, representing the four different levels of interaction between these processes, i.e., at the molecular level, the cellular level, the tissue/organ levels, and finally the clinical level. Where these pivotal interactions occur for individual pharmacologic classes determine the four systems therapeutics categories. These two processes are initiated by an interaction between a pharmacologic agent and a pharmacologic response element on one hand and a hypothetical intrinsic operator and an etiologic causative factor on the other, and then culminate in a therapeutic response. Each of these two processes, pharmacologic and pathophysiologic processes, have their inherent variabilities; this construct further suggests that in addition to pharmacologic processes, pathophysiologic processes also contribute to ultimate patient therapeutic response characteristics.

Ongoing and Future Directions

Progress to-date has focused on generating work on each of the three initial projects, and uploading these to the TRI-institute.org website. Work on the Characteristics of Therapeutic Response project has been hampered by the challenges in identifying appropriate publicly available databases and the best reporting formats. The most recent efforts have focused on the Systems Therapeutics project, including the recently updated diagram, and current and future efforts will include additional work in this area, e.g., examples and exhibits, as well as strengthening collaborative outreach.

2015 Report: Progression of Modern Therapeutics

The 2015 Report of Progression of Modern Therapeutics provides graphs of new drug approvals for 40 therapeutic classes from 40 therapeutic categories. These include the 25 therapeutic classes that were in the 2014 Report, any updates on these, and 15 new ones, including irritable bowel syndrome, HIV-1/AIDS, hepatitis C, malaria, melanoma, pediatric acute lymphoblastic leukemia. Click here for the 2015 Report on this website.

Comments

As with most projects there are sometimes unexpected issues that come up from time to time, and this project is no different. We will comment on three such issues at this time:

Missing approved new drugs. Since the approach used for what new drug approvals to include involves a therapeutic class centered approach rather than an NME centered approach, it is possible there may be missing drug approvals, since these may involve secondary indications of NME’s or subsequent approvals of the same compound, and would thus not be included in FDA’s annual NME listings. Any such omissions will be corrected as appropriate.

Missing approval dates. For several older drugs, the exact initial approval date may not be listed in the Drugs@FDA database, or other readily available databases. In such cases, a footnote is added to that effect on the graphs for individual therapeutic classes, e.g., pediatric acute lymphoblastic leukemia and migraine; other examples would include tuberculosis.

Off-label use. For some diseases there is considerable off-label use, and a number of drug information websites may include such use, without necessarily distinguishing between FDA approved drugs and off-label use, e.g., migraine. Such examples of clinical use are typically not included on the graphs.

Random Observations and Comparisons

A perusal of this 2015 Report suggests a few lessons learned, including the following random observations and comparisons:

Note significant recent advances in new drug approvals for hepatitis C, melanoma, cystic fibrosis, idiopathic pulmonary fibrosis, and irritable bowel syndrome, to name just a few.
Note recent introductions of new pharmacologic classes or mechanisms of action for type-2 diabetes, dyslipidemia, multiple sclerosis, pulmonary arterial hypertension, and rheumatoid arthritis, to name just a few.
Note no new introductions of new pharmacologic classes for depression (MAO inhibitors, tricyclics, SSRI’s/SNRI’s) and schizophrenia (typical and atypical antipsychotics) since the late 1980’s.
Contrast melanoma with 8 new drug approvals since 2010 with childhood acute lymphocytic leukemia with most drugs from the 1950’s, 1960’s and 1970’s.
Contrast plaque psoriasis with 7 new drug approvals with systemic lupus erythematous with 1 new drug approval since 2000.
What do thrombolysis and acid reflux/gastric ulcer have in common? No new drug approvals since 2000/2001.

The two prior annual update reports (2013 Report and 2014 Report) contained different summary graphics, which are not included in the 2015 Report; these will be addressed in future posts.

2015 Update on Current Projects

Introduction

The Therapeutics Research Institute, a 501(c)(3) nonprofit corporation, was initiated to examine and report on various aspects of modern therapeutics. Its website, TRI-institute.org, which contains its reports and commentaries, became operational in August 2014.

Update on Current Projects

Progress on the three initial projects of the TRI-institute and planned work are summarized in the chart below (click for a larger chart).

Progression of Modern Therapeutics

This primary focus of this project involves the development of a database, graphics and reports of new drug approvals for individual therapeutic classes, sorted by pharmacologic classes and years of approval. The latest published report (2014 Report) includes 25 therapeutic classes. A separate report has been published on orphan drug approvals for metabolic disorders. Furthermore, a total of eight brief commentaries have been posted on individual therapeutic classes that were already addressed in the reports.

Planned work includes 1) updating the annual reports, which will also be expanded to include additional therapeutic classes, including oncologic therapeutics, exemplified by childhood malignancies, 2) exploring relationships and lessons across therapeutic and pharmacologic classes, and 3) continuing commentaries on selected individual therapeutic classes.

Therapeutic Response Characteristics

This project focuses on the development of a database involving therapeutic response characteristics of approved new drugs, including therapeutic response variability and range, from minimum and average to maximum responses. An initial project outline has been proposed, and a commentary has been posted on the ultimate positive response, i.e., cure.

Planned work includes 1) developing a pilot project on therapeutic response characteristics of several therapeutic classes, selected from those completed under the project on “Progression of Modern Therapeutics”, and 2) proposing a summary of the ideal therapeutics performance parameters and measures.

Systems Therapeutics

This project focuses on the interactions of pharmacologic and pathophysiologic processes resulting in a therapeutic effect. An initial diagrammatic framework has been proposed, which includes four systems therapeutics categories, ranging from the molecular level, through the cellular and tissue/organ level, to the clinical level.

Planned work includes 1) developing several examples of individual pharmacologic and pathophysiologic interactions, involving each of the systems therapeutics categories, for several therapeutic and pharmacologic classes, and 2) making any refinements or additions to the initial diagrammatic framework, in part based on learnings from the examples developed.

Comments

In 1980, Dr. Paul P. Beeson published a paper in the journal Medicine (1), where he reviewed and compared treatments recommended in the first (1927) and fourteenth (1975) editions of the Cecil Textbook of Medicine (2). In 1927, only 6% of diseases had treatments considered to be “effective”, “helpful”, or “highly effective”, whereas in 1975, half a century later, this percentage had increased to 50%.

It is noted that the project on “Progression of Modern Therapeutics” starts approximately around the time Dr. Beeson’s assessment ends. It is also noted that in the time period between 1976 and 2014, a total of approximately 1,000 new drugs have been approved by FDA, i.e., meeting regulatory requirements for approval, and importantly, these include the majority of today’s effective treatments. These considerations emphasize the importance of continuing examination of various aspects related to the evolution of modern therapeutics, and what can be learnt from those. These are the fundamental objectives of the Therapeutics Research Institute, that led to its initial three projects.

References

1) Beeson PP. Changes in medical therapy during the past half-century. Medicine, 59:79-99, 1980.

2) Beeson, PP, McDermott W, Cecil RL. Textbook of Medicine, Edition 14, Saunders, Ken & Georgie, 1975.

Refer to other section on this website, particularly Projects, Reports, and Posts.

TRI-institute’s website is alive

Finally after some trials and errors, the website for the Therapeutics Research Institute is up and running. This is a rather simple website for now, but we hope it will gradually have more content, as the projects evolve.