Tag Archives: drug development

Likelihood Ratios in Assessing the Safety of New Medicines

Robert A. Coleman

Until we know the true predictive value of animal-based methods
for predicting clinical safety issues, it is impossible to assess
the advantage or otherwise of non-animal based approaches

The use of animals in the discovery and development of new medicines has generated debate for decades. For much of this time, contrasting views have been primarily polarised on the basis of practicality versus ethics, with proponents arguing that for the development of new medicines to treat human disease, the end justifies the means, while the opponents’
chief objection has been the associated animal suffering. Commercial and public health pressures over the years have ensured that those in the ‘practicality camp’ have held sway.

More recently, however, the issue has become increasingly complex, with growing concerns that, irrespective of ethical considerations, data generated in animal (i.e. non-human) models are not necessarily or sufficiently relevant to human patients.1–3 There is now general consensus that inter-species variability is a real issue, and that animal models are far from perfect for the purpose of ensuring either the efficacy or the safety of potential new medicines intended for human subjects. Supporters of the continued use of animals argue that, while they do not provide an absolute indication of either efficacy or safety, in the absence of any other approach, one that is somewhat unreliable is better than none at all.

Such an argument has some merit, if indeed it is valid. However, in this field, all may not be entirely as it seems. Firstly, human-based in vitro and in silico alternatives are becoming ever more sophisticated,4 thus overcoming many of the criticisms originally directed toward them. For example, it has long been held that it would be impossible to model the complexity of the intact patient through a study of isolated cells and tissues, and while this problem may never be wholly overcome, the gap gets ever smaller. Secondly, it is important to understand that we really don’t know how good existing animal-based methods are. In the field of efficacy, there is a wealth of evidence that results obtained by using experimental animals can be hugely misleading.5–11 Here, we have the advantage that drugs that promise efficacy in patients on the basis of animal data can advance into clinical testing, and their utility can be directly assessed. For the majority of these drugs, the clinical outcome has been disappointing. With safety, the issue is different, as drugs with identified safety issues in animals will seldom, if ever, advance to clinical testing, thus the relevance of the animal data to safety in humans may never be determined. However, what we do know is that many drugs identified as safe in pre-clinical profiling eventually prove to cause serious and use-limiting side effects in human subjects. 12 The key question is, “Could such failures have been avoided, had we relied on human-based test methods?” Until we know how frequently non-animal methods could have identified safety issues that were missed by animal tests, it is impossible to assess the advantage or otherwise of those methods. It is a fact that, despite the continued use of animals as human surrogates in pharmaceutical research, there has never been a solid, published, peer-reviewed study demonstrating fitness for purpose, whereas reviews identifying the shortcomings are abundant.

Assessing the value of animal studies

It is for this reason that any information that sheds light on the actual value of animal-based testing for its intended purpose is of inestimable worth. Until recently, much evidence, while valuable, has been indirect. For example, a recent telling study demonstrated that pre-clinical fast-tracking (i.e. abbreviated safety testing) of potential new medicines resulted in no increase in the proportion of candidates that subsequently proved toxic in human subjects.
13 In another study, the ability of animal studies to detect serious post-marketing adverse events was demonstrably poor.14 While such reports add to the volume of data providing witness to the shortcomings of animal-based approaches to ensuring clinical safety, they do not provide a robustly measurable metric of predictive efficiency. In view of the colossal amount of data generated over the years in pre-clinical safety studies on thousands of new potential medicines, many of which have progressed to clinical testing and even to market, it is amazing that, until recently, no comprehensive analysis of such data has been applied in order to explore the value of the current approach to safety testing.

Likelihood ratios

In the light of such a background, it is of considerable significance that serious attempts are now being made to extract intelligence from the wealth of information available in publicly accessible sources, in order to shed more light on the actual predictive power of animal-based safety testing. A particular example is the utilisation of the Safety Intelligence Programme (SIP),15 which overcomes semantic issues to extract valid information from all available data sources. SIP has been used, for example, to explore the predictive power of animal models for the detection of liver toxicity associated with a wide range of human medicines,16 highlighting the highly variable efficiency of different models in combination with different drug toxicities. More recently, SIP has been used to particular effect in two studies that have explored directly the value of dogs, mice, rats and rabbits in predicting safety issues in human subjects.17,18 While most previous studies have relied on determining ‘concordance’ between animal and human data, that tells only a part of the story, and is too simplistic a measure to be of much real value. Its
problem is that it only deals with positive correlation, i.e. the frequency that toxicity in experimental animals and in human subjects coincide, ignoring the issue of true prediction. What is needed is a determination of likelihood ratios (LRs),19 both positive (PLRs) and negative (NLRs), to gain a more complete picture. What emerged when LRs were determined was that, although there was indeed some measure of concordance between positive toxicity data between animals and humans, in terms of LRs, none of the species proved to offer any useful level of real predictive power. Although the studies and their conclusions did not escape criticism from some quarters,20 the suggested limitations, real or perceived, are arguably irrelevant to its overall validity.21

What did emerge from the application of this approach were absolute values for both PLRs and NLRs for a wide range of specific drugs. The importance of this is that, for the first time, such measures can provide a robust yardstick against which to evaluate the relative merits of alternative approaches to toxicity testing.

Conclusions

To summarise, the use of more-rigorous approaches to the evaluation of animal models as predictors of the likelihood that any chemical will be similarly toxic or non-toxic in human subjects provides not only a realistic measure of their actual fitness for purpose, but crucially, also a basis by which the efficiency of other, ideally human-based, approaches can be evaluated through their exposure to the same range of drugs. The use of the same drugs ensures that any criticisms related to potential bias, or other potentially confounding factors, are negated. Such a prospective study would be of inestimable value. Dare we hope that the government and pharmaceutical companies will take up the challenge and fund
such a study?

Dr Robert A. Coleman
Independent Consultant
UK
E-mail: robt.coleman@btinternet.co
m

References

1 Wall, R.J. & Shani, M. (2008). Are animal models as good as we think? Theriogenology 69, 2–9.
2 Hartung, T. (2013). Food for Thought… Look back in anger — What clinical studies tell us about preclinical work. ALTEX 30, 275–291.
3 Pippin, J.J. & Sullivan, K. (undated). Dangerous medicine:
Examples of animal-based “safety” tests gone wrong. Washington, DC, USA: The Physicians Committee for Responsible Medicine. Available at: http://www.pcrm.org/research/animaltestalt/animal testing/dangerous-medicine-examples-of-animalbased-tests (Accessed 03.11.14).
4 Ashton, R., De Wever, B., Fuchs, H.W., Gaça, M., Hill, E., Krul, C., Poth, A. & Roggen, E.L. (2014). State of the art on alternative methods to animal testing from an industrial point of view: Ready for regulation? ALTEX 31, 357–363.
5 Pound, P., Ebrahim, S., Sandercock, P., Bracken, M.B. & Roberts, I. (2004). Where is the evidence that animal research benefits humans? British Medical Journal 328, 514-517.
6 Kaste, M. (2005). Use of animal models has not contributed
to development of acute stroke therapies: Pro. Stroke 36, 2323–2324.
7 Pippin, J.J. (2005). The Need for Revision of Pre-Market Testing: The Failure of Animal Tests of COX-2 Inhibitors, 23pp. Washington, DC, USA: The Physicians Committee for Responsible Medicine. Available at: pcrm.org/pdfs/research/testing/exp/COX2Report.pdf
(Accessed 04.11.14).
8 Hackam, D.G. & Redelmeier, D.A. (2006). Translation of research evidence from animals to humans. Journal of the American Medical Association 296, 1731–1732.
9 Knight, A. (2008). Systematic reviews of animal experiments
demonstrate poor contributions towards human healthcare. Reviews on Recent Clinical Trials 3, 89–96.
10 Matthews, R.A.J. (2008). Medical progress depends on animal models — doesn’t it? Journal of the Royal Society of Medicine 101, 95–98.
11 Seok, J., Warren, H.S., Cuenca, A.G., Mindrinos, M.N., Baker, H.V., Xu, W., Richards, D.R., McDonald-Smith, G.P., Gao, H., Hennessy, L., Finnerty, C.C., López, C.M., Honari, S., Moore, E.E., Minei, J.P., Cuschieri, J., Bankey, P.E., Johnson, J.L., Sperry, J., Nathens, A.B., Billiar, T.R., West, M.A., Jeschke, M.G., Klein, M.B., Gamelli, R.L., Gibran, N.S., Brownstein, B.H., Miller-Graziano, C., Calvano, S.E., Mason, P.H., Cobb, J.P., Rahme, L.G., Lowry, S.F., Maier, R.V., Moldawer, L.L., Herndon, D.N., Davis, R.W., Xiao, W., Tompkins, R.G. & Inflammation and Host Response to Injury, Large Scale Collaborative Research Program (2013). Genomic responses in mouse models poorly mimic human inflammatory diseases. Proceedings of the National Academy of Sciences of the USA 110, 3507–3512.
12 Li, A.P. (2004). Accurate prediction of human drug toxicity: A major challenge in drug development. Chemico-biological Interactions 150, 3–7. 13 Arnardottir, A.H., Haaijer-Ruskamp, F.M., Straus, S.M., Eichler, H.G., de Graeff, P.A. & Mol, P.G. (2011). Additional safety risk to exceptionally approved drugs in Europe? British Journal of Clinical Pharmacology 72, 490–499.
14 van Meer, P.J., Kooijman, M., Gispen-de Wied, C.C., Moors, E.H. & Schellekens, H. (2012). The ability of animal studies to detect serious post marketing adverse events is limited. Regulatory Toxicology & Pharmacology 64, 345–349.
15 Berry, S. (2012). Safety Intelligence Program Provides Insight into Drug-Induced Cardiac Effects. Stone, UK: Instem plc. Available at: http://www.biowisdom.com/content/safety-intelligence-program (Accessed 04.11.14).
16 Fourches, D., Barnes, J.C., Day, N.C., Bradley, P., Reed, J.Z. & Tropsha, A. (2010). Cheminformatics analysis of assertions mined from literature that describe drug induced liver injury in different species. Chemical Research in Toxicology 23, 171–183.
17 Bailey, J., Thew, M. & Balls, M. (2013). An analysis of the use of dogs in predicting human toxicology and drug safety. ATLA 41, 335–350.
18 Bailey, J., Thew, M. & Balls, M. (2014). An analysis of
the use of animal models in predicting human toxicology and drug safety. ATLA 42, 181–199.
19 Altman, D.G. & Bland, J.M. (1994). Diagnostic tests 2: Predictive values. British Medical Journal 309, 102.
20 Brooker, P. (2014). The use of second species in toxicology
testing. ATLA 42, 147–149.
21 Bailey, J. (2014). A response to the ABPI’s Letter to
the Editor on the use of dogs in predicting drug toxicity
in humans. ATLA 42, 149–153.

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Lost in Translation: The Need for Better Tools

Susanna Penco, Elena Venco and Alfredo Lio

Although for most pharmaceutical compounds
the final aim is improving human health,
almost all the methods used to identify and
pursue therapeutic targets and to obtain
new potential drugs have traditionally
focused on animal models

Introduction
Recent studies on attrition rate in pharmacological research show that the pharmaceutical industry finds it difficult to turn new experimental compounds into safe and effective drugs. Although, for most pharmaceutical compounds, the final aim is improving human health, almost all the methods used to identify and pursue therapeutic targets and to obtain new potential drugs have traditionally been centered on animal models. The ability of such methods to predict efficacy and safety in humans needs to be carefully reviewed, in the light of more-predictive and more reliable human-based experimental tools.

The overall cost for the development and the marketing of a new pharmaceutical product ranges between one billion and 1.8 billion US dollars.1 It has been estimated that only one in 10,000 new chemical entities (NCEs), also referred to as new molecular entities (NMEs), discovered in the laboratory succeeds in obtaining marketing approval.2, 3 Recent studies have shown that 95% of experimental drugs fail in the clinical phase.4, 5 The main reasons for these poor results can be ascribed to the lack of therapeutic efficacy and safety.6, 7 Such findings point to the significant inadequacy of the current preclinical tests — mainly in vitro cellular assays and animal based disease models — in screening pharmacological compounds. Many in vitro tests are still performed in a two-dimensional format,8 despite the limitations of this approach,8, 9 and are based on animal cells, which complicates the potential extrapolation of the information they provide to humans.6, 10, 11 In spite of this, such preclinical methods are still considered the ‘gold standard’ in pharmaceutical R&D.

The relevance of animal models
Many animals, including mice, rats, dogs, cats, and non-human primates (NHPs), are used in biomedical and toxicological research as human disease surrogates, so they are defined as ‘animal models’. However, there are a number of important limitations that underlie the lack of successful use of these animal models in furthering the understanding of human disease.

Firstly, there are significant differences among species with regard to their anatomy, metabolism and physiology, which correspond to genetic differences, including differences in regulatory genes. This means that even slight molecular differences can be amplified in the extrapolation process from one species to another. For instance, mice (together with rats, which are the most commonly used species in biomedical research) share with humans slightly more than 90% of their gene sequences. Nevertheless, at least 67 major discrepancies have been found in the immunological functions of mice and humans. This fact is hardly surprising, since these two species separated approximately 65- to 75-million years ago, and have since followed different evolutionary path ways.

About 1% of human genes do not have a homologue in the mouse.14 Biochemistry provides many examples concerning similarities and differences between species. Some of the most significant differences are in the cytochrome P450 enzymes (CYPs), which seem to have evolved from a single ancestral gene over a period of 1.36 billion years. To date, at least 14 families of CYPs genes have been identified in mammals.15 Each member of this gene family has many highly conserved regions in its secondary amino acid structure. However, remarkable differences between species also exist in the primary amino acid sequences. Even small differences in amino acid sequence can imply wide differences in substrate specificity.16 Such variations can explain the divergences in drug response between animal models and humans. The scientific literature provides many examples of therapies that proved successful in animal models, but subsequently failed in clinical trials.17-20

A second important issue surrounding the failure of many animal models is the way in which the disease is induced. Diseases induced ‘artificially’ in animals cannot begin to accurately reproduce the very complex aspects and conditions clinically observed in human patients. This is thought to be one of the most crucial reasons for drug attrition.21, 22

In addition, there are relevant species-specific differences in absorption, distribution, metabolism, excretion and toxicity (ADMET) between animals and humans.23 These processes together make up the important concept of ‘pharmacokinetics’.

Pharmacokinetics is one of the main reasons for candidate compound failure in humans.24 A wide range of species-specific metabolic patterns strongly suggest that data can be hardly (at best) extrapolated from one species to another, both quantitatively and qualitatively — i.e. differences in the amino acid sequence of isozymes may influence both the rate of drug metabolism and the metabolite pattern. 25 An outstanding example of species-specific differences between rats and humans is in coumarin metabolism and toxicity, which appears to be mediated through two major phase I metabolic pathways. The first pathway, involving cytochrome CYP2A enzymes and leading to the conversion of coumarin into the non-toxic metabolite 7-hydroxycoumarin, is very efficient in humans and extremely inefficient in rats. The second pathway involves the detoxification of the epoxide intermediate, coumarin 3,4-epoxide, which spontaneously rearranges to o-hydroxyphenylacetaldehyde and is then oxidised to o-hydroxy – phenylacetic acid. In rats, the rate of conversion to o-hydroxyphenylacetic acid is 50 times lower than in humans. These metabolic discrepancies explain the differences in coumarin-mediated hepatotoxicity between the two species.26

There are many significant examples of drug attrition resulting from the limitations of the animal models used in pharmaceutical R&D:
— More than 150 experimental therapeutics for the treatment of sepsis have been successfully tested in animals. None of them proved useful in humans.27
— A total of 800 new drugs showed promising results in animal models for stroke, but only 97 were approved for the clinical phases. Unfortunately, only two showed some efficacy, with aspirin being one of the two.28, 29
— More than 85 different HIV vaccines have been tested in approximately 200 clinical studies,30 but to date no therapeutic or protective effects on humans have been found. The use of resources has been so extensive that, even if an effective HIV vaccine were found as a result of animal experimentation, animal models could not be considered a suitable predictive experimental method, since the PPV (positive predictive value) would be around 0.01.31

The list of failures gets longer with anti-cancer drugs, and there is also an endless list of failures in relation to neurodegenerative diseases. Indeed, anticancer drugs and treatments for neurological diseases have the highest attrition rate in the development process.32 Awareness of the limits of the predictivity of animal models is rapidly growing.33-39 Even the use of transgenic animals seems to have proved inconclusive in translational medicine.6, 34, 40-42 With regard to neurodegenerative diseases, the results obtained by testing new therapies on animals have been very poor.17, 43, 47

The study of bioavailability is a clear example of the differences in drug response occurring among species, as shown by many studies.48-51 Systematic reviews of the predictive accuracy of animal models in the field of teratogenesis52, 53 and carcinogenesis,54 also showed poor predictive power. In a recent analysis of the registration files of all therapeutic monoclonal antibodies (tmAbs) available in the EU, van Meer et al.55 discovered that the incidence of formation of anti-tmAb antibodies in NHPs and patients was comparable in only 59% of cases. In addition, the type of anti-tmAb antibody response was different in NHPs and humans in the same proportion of cases. The authors concluded that monoclonal antibody immunogenicity in NHPs and humans is significantly different.

In a recent review of the use of the dog model and other animal models in drug toxicology, the authors concluded that its predictive value in current toxicology was very poor.56, 57 The issues associated with extrapolating data from animals to humans are probably due to both inadequate testing procedures and to the failure of models to accurately reproduce human diseases, but evidence is growing that the core of the problem could only be resolved by giving up the use of animals as models.33 Therefore, in the light of controversial predictive value, it is not surprising that some scientists consider preclinical animal studies, “generally scarce, unreliable or nonpredictive”. 58-60

Considering the present stalemate of translational medical science, the development of new, reliable experimental approaches to assure efficiency, convenience and safety in clinical therapies is desperately needed. Long-term Multicentre Evaluation of In Vitro Cytotoxicity (MEIC) studies, comprising a set of in vitro tests based on human biological materials, proved more predictive in testing compounds than did traditional animal-based acute toxicity studies.61
Recently, many important improvements have been made in studying acute toxicity, repeated toxicity62 and reproductive toxicology, as assessed by the ESNATS report.63-64 One particularly promising field is that of organs-on-chips, which are micro-engineered physiological systems aimed at reproducing the physiological properties of human tissues and organs and their interactions. Thanks to these biochips, it has been possible to create a model for acute pulmonary oedema that has permitted the evaluation of new clinical and therapeutic interventions.65 In addition to the lung-on-a-chip, other tools have been successfully developed to mimic the human gut66 and kidney.59 The final aim is clearly to develop a ‘human-on-a-chip’, to fully mimic the functions of and interactions between organs, thus getting closer to the human in vivo situation. Indeed, some already trust this approach as a valid alternative to traditional animal tests.67-70 In addition, the use of human pluripotent stem cells seems to be becoming more widely appreciated in pre-clinical toxicology.71-72

Conclusions
Since the available data show that their predictivity can no longer be assumed, there is an urgent need for reviews and meta-analyses of the animal models currently used in medical research. Moreover, science should focus on the development of more-advanced methods, as a result of the limitations of the current pre-clinical tools, the growing bioethical objections surrounding their use, and the ongoing development
of new in vitro and in silico techniques. These alternative methods should be used ideally in the experimental context of an Integrated Testing Strategy.

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