Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5436_Библиотеки_им_академика_М_И_Перельмана.pdf
100
Qasim W, Zhan H, Samarasinghe S, Adams S, Amrolia P, Stafford S, Butler K, Rivat C (2017)
Molecular remission of infant B-ALL after infusion of universal TALEN gene-edited CAR T
cells. Sci Transl Med 9:eaaj2013
Qu L, Yi Z, Zhu S etal (2019) Programmable RNA editing by recruiting endogenous ADAR using
engineered RNAs. Nat Biotechnol 37:1059–1069
Rajkomar A, Oren E, Chen K etal (2018) Scalable and accurate deep learning with electronic
health records. Npj Digit Med 1:1–10
Reautschnig P, Wahn N, Wettengel J etal (2022) CLUSTER guide RNAs enable precise and ef-
cient RNA editing with endogenous ADAR enzymes invivo. Nat Biotechnol 40:1–10
Roth TL, Puig-Saus C, Yu R etal (2018) Reprogramming human T cell function and specicity
with non-viral genome targeting. Nature 559:405–409
Saah AJ, Hoover DR (1997) “Sensitivity” and “specicity” reconsidered: the meaning of these
terms in analytical and diagnostic settings. Ann Intern Med 126:91–94
Santoni de Sio FR, Passerini L, Valente MM, Russo F, Naldini L, Roncarolo MG, Bacchetta R
(2017) Ectopic FOXP3 expression preserves primitive features of human hematopoietic stem
cells while impairing functional T cell differentiation. Sci Rep 7:15820
Sato Y, Passerini L, Piening BD, Uyeda MJ, Goodwin M, Gregori S, Snyder MP, Bertaina A,
Roncarolo M-G, Bacchetta R (2020) Human-engineered Treg-like cells suppress FOXP3-
decient T cells but preserve adaptive immune responses invivo. Clin Transl Immunol 9:e1214
Sayitoglu EC, Freeborn RA, Roncarolo MG (2021) The Yin and Yang of Type 1 regulatory T cells:
from discovery to clinical application. Front Immunol 12:693105
Smith AJ (2015) New horizons in therapeutic antibody discovery: opportunities and challenges
versus small-molecule therapeutics. J Biomol Screen 20:437–453
Stafforst T, Schneider MF (2012) An RNA–deaminase conjugate selectively repairs point muta-
tions. Angew Chem Int Ed 51:11166–11169
Stumpfe D, Bajorath J (2020) Current trends, overlooked issues, and unmet challenges in virtual
screening. J Chem Inf Model 60:4112–4115
Torng W, Altman RB (2019) Graph convolutional neural networks for predicting drug-target inter-
actions. J Chem Inf Model 59:4131–4149
U.S.Food and Drug Administration. Center for Drug Evaluation and Research (CDER). Botanical
Drug Development: Guidance for Industry (2016) Silver Spring. Available at: https://www.fda.
gov/media/93113/download
Vogel P, Moschref M, Li Q, Merkle T, Selvasaravanan KD, Li JB, Stafforst T (2018) Efcient and
precise editing of endogenous transcripts with SNAP-tagged ADARs. Nat Methods 15:535–538
Wilson JL, Gravina A, Grimes K (2022) From random to predictive: a context-specic interaction
framework improves selection of drug protein–protein interactions for unknown drug path-
ways. Integr Biol 14:13–24
Wu G, Yuan Y, Hodge CN (2003) Determining appropriate substrate conversion for enzymatic
assays in high-throughput screening. J Biomol Screen 8:694–700
Wu C, Lee S-L, Taylor C, Li J, Chan Y-M, Agarwal R, Temple R, Throckmorton D, Tyner K (2020)
Scientic and regulatory approach to botanical drug development: a U.S.FDA Perspective J
Nat Prod 83:552–562
Yang B, Mallett S, Takwoingi Y etal (2021) QUADAS-C: a tool for assessing risk of bias in com-
parative diagnostic accuracy studies. Ann Intern Med 174:1592–1599
Yi Z, Qu L, Tang H et al (2022) Engineered circular ADAR-recruiting RNAs increase the ef-
ciency and delity of RNA editing invitro and invivo. Nat Biotechnol:1–10
Yuan B, Shen C, Luna A, Korkut A, Marks DS, Ingraham J, Sander C (2021) CellBox: interpre-
table machine learning for perturbation biology with application to the design of cancer com-
bination therapy. Cell Syst 12:128–140.e4
Zhang J-H, Chung TDY, Oldenburg KR (1999) A simple statistical parameter for use in evaluation
and validation of high throughput screening assays. J Biomol Screen 4:67–73
D. Mochly-Rosen et al.
101
3
On theWay totheClinic
DariaMochly-Rosen, KevinGrimes, C.GlennBegley,
DirkMendel, WernerRubas, EmilyEgeler,
CollenMasimirembwa, TerrenceF.Blaschke,
andMichaelTaylor
D. Mochly-Rosen (*) · K. Grimes
Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, US
e-mail: sparkmed@stanford.edu; kgrimes@stanford.edu
D. Mendel
SPARK at Stanford, Stanford University School of Medicine, Stanford, CA, US
C. G. Begley
SPARK at Stanford Advisor, Stanford, CA, US
E. Egeler · T. F. Blaschke
Stanford University School of Medicine, Stanford, CA, US
W. Rubas
Sutro Biopharma, Inc., South San Francisco, CA, US
C. Masimirembwa
African Institute of Biomedical Science and Technology, Harare, Zimbabwe
M. Taylor
NonClinical Safety Assessment, Mountain View, CA, US
Daria Mochly-Rosen, Ed, Kevin Grimes, Ed.
Once a “hit” drug has been identied, it is important to evaluate it in appropriate
animal models for the indicated pathology and to apply the highest standards of
research to ensure the integrity of the ndings. When performing drug discovery
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
D. Mochly-Rosen, K. Grimes (eds.), A Practical Guide to Drug Development in
Academia, https://doi.org/10.1007/978-3-031-34724-5_3
102
research, we must be particularly attentive to the robustness of our experiments,
because inability to reproduce academic data continues to be a sticking point when
projects are transferred to industry. Our experiments must be appropriately blinded,
statistically powered, and meticulously documented so that our ndings are worthy
of the large investment required for further translation into a drug.
This chapter walks through the essential preclinical drug development steps to
ensure that the compound has optimal pharmacological features. These features
include understanding how the drug is working on the body (pharmacodynamics;
PD) and what the body does to the drug (the drug’s absorption, distribution,
metabolism, and excretion, a eld collectively termed pharmacokinetics; PK). We
also must remember that whereas animal models are often genetically identical, the
great genetic variation among humans must be considered when developing a drug.
Genetic variations in the molecular target of the drug—its PD, as well as in enzymes
and proteins that affect its PK, are critical to evaluate.
Early evaluation of these characteristics will increase the probability that clin-
ical trials will succeed. If a subset of patients has a protein target that is not rec-
ognized by the drug, their inclusion in the trial will reduce the overall benet of
the tested drug. Similarly, genetic variations in drug absorption, metabolism,
and/or secretion may result in lack of response or unacceptable side effects of the
tested drug. Unfortunately, these latter features are often discovered after drug
approval, yet patient suffering could have been readily prevented by introducing
pharmacogenomics studies during drug development. Drug formulation and
route of administration must be established to maximize treatment efcacy and
ease of use for the patient and physician. Lastly, preclinical safety studies must
be conducted to predict potential drug toxicities and establish safe dosing ranges.
These topics are rarely addressed in traditional academic drug discovery efforts.
We hope that you will incorporate them into your research plan, because without
such considerations, even a great drug is likely to fail.
3.1 When toBegin Animal Studies
DariaMochly-Rosen
We have identied a new chemical entity or a known drug that affects our validated
target/pathway and has shown its efcacy in a cell-based assay. What is the next step?
Experts are divided on whether it is advisable to begin animal studies right away
or whether it is better to rst identify the optimal compound. On one hand, by
generating and testing analogs of the original “hit,” it may be possible to improve
potency or specicity for the target. In vitro studies to obtain an optimal formulation
for a drug or simply increase solubility can also improve the chance for success
once animal studies begin. There are other considerations, such as invitro assess-
ment of drug toxicity and metabolism, including liver enzyme assays, human ether-
à-go- go-related gene (hERG) channel effects, etc. In other words, we can easily
spend a year and thousands of dollars in studies aimed at improving our initial hit.
On the other hand, invitro and cell-based assays are usually cheaper and faster
to run than animal studies, but they are not always predictive of the invivo behavior
D. Mochly-Rosen et al.
103
of the molecule—which is ultimately most important for determining if our hit will
make a good drug. So, how are drug development programs to decide, with their
limited funds, between screening many analogs invitro versus testing only a handful
of molecules in animals? As an academic that has followed over 230 programs
within SPARK at Stanford, my answer to this question is simple.
3.1.1 Take aShortcut
We should start animal studies as soon as we can. It is true that many improvements
to our compound can be made, but a short invivo study can be extremely valuable
in helping to optimize the compound and induce greater interest from partners and
investors. A great deal can be learned from an imperfect drug. We might even be
lucky and nd that our compound shows a therapeutic benet and drug-like
properties!
We must also recognize that failure to demonstrate efcacy at this stage is not a
reason to discontinue our project. These are exploratory studies, and much can still
be done to improve the compound’s selectivity, potency, solubility, bioavailability,
safety, metabolism, route of administration, and nal formulation. The “take a
shortcut” approach is illustrated in Fig.3.1, with key concepts further discussed in
this section.
3.1.2 What Animal Model toUse?
It is best to read the literature and use an animal model that is accepted in the eld
for the given indication. It is inadvisable to develop a new model for this rst invivo
trial. Better yet, we can nd a collaborator that is using this animal model and have
them do the study for us. It is rare that such a study will generate new intellectual
Fig. 3.1 When to begin animal studies. Abbreviations: ip Intraperitoneal, sc Subcutaneous
3 On theWay totheClinic
104
property—and the collaborator can provide an independent and unbiased assessment
of our compound. It is worth noting that the animal model used for safety studies
may not be the same model used for efcacy studies (for more on animal models,
refer to Sect. 3.3.3). Additionally, despite limited nancial resources, we must make
sure that our animal studies are robust enough. Review Sect. 3.2, Robustness of
Preclinical Studies, before embarking on this important part of your program.
3.1.3 How toDeliver theDrug?
Even if we believe that oral administration is the ideal route for our clinical indica-
tion, it is ill-advised to attempt to do the rst efcacy study in animals using oral
gavage. Instead, consider intraperitoneal (ip) injection. If the drug is not very solu-
ble, we can deliver the drug with ethanol, DMSO, or polyethylene glycol; animals
will tolerate quite a high dose of these solvents. Consult your animal facility for
what is well tolerated in your animal species. If there is concern that the drug dose
will be too low or exposure too short using ip injection, consider using a subcutane-
ous (sc) Alzet® osmotic pump. The company’s website details various sizes and
recommended solvents as well as training on how to implant them.
3.1.4 Start withaSmall Safety Study
To make sure that the drug dose is not fatal, inject a couple of healthy animals and
observe them for a few hours or days for obvious signs of toxicity. A veterinary
nurse or technician can help with monitoring for adverse events. Once we know that
the dose selected is not acutely toxic, we can jump into efcacy and longer safety
studies in the chosen animal model of disease.
3.1.5 Learn asMuch asYouCan fromtheFirst (Pilot)
InVivo Study
Animals are precious and should be used sparingly. Therefore, plan your experi-
ments carefully to include proper controls. For a rst pilot study, it is okay to just
compare drug-treated to nontreated animals. However, it is better to also include a
drug that is known to be efcacious in the model—if one is available—as a positive
control and a vehicle as a negative control. When euthanizing the animals, collect as
many organs and bio-uids as possible for analysis. A pathologist can advise how to
Box 3.1: What Surprised an Academic?
When selecting a delivery formulation for these initial animal studies, simpler
is better. We once used an over the counter beauty lotion as the vehicle for an
initial topical delivery study because it had the desired aqueous formulation
properties. —DM-R
D. Mochly-Rosen et al.
105
preserve the tissues and store samples for later analysis. We should attempt to collect
as much data as possible relevant to our disease and to compound safety. The results
will be most helpful if drug concentrations in the blood (and other relevant tissues)
can be determined for each dose administered. The bottom line: We need to
maximize the information obtained from this rst set of studies.
3.1.6 If theShortcut Failed
We are not done! Remember that we have committed to take the long route even if
the shortcut failed. We can go back and perform further structure–activity
relationship (SAR) studies with analogs of our hit and additional studies on drug
solubility and in vitro toxicity. We can now focus on correcting the problems
identied based upon the rst invivo experiment.
3.1.7 If theShortcut Succeeded
Congratulations! The work has just begun. Now we have more compelling data that
the project is worth pursuing. Make sure to consult Sect. 3.2 on robust preclinical
work and Sect. 3.3 on invivo pharmacology to plan your next steps.
Later in this chapter, you will learn that exploring pharmacokinetics (PK) prior
to efcacy studies has many advantages. In SPARK, we provide multiple, and
sometimes what appears to be opposing opinions, as there is no single path for drug
development. After reading both sections, it is up to you to decide whether you will
perform efcacy studies rst (as described above), or whether you will conduct
proper PK studies prior to efcacy studies; both eventually need to be completed.
However, based on the resources available to you, you will need to choose the order
best suited for your project.
3.2 Robustness ofPreclinical Studies
DariaMochly-Rosen and C.GlennBegley
A number of publications challenge the robustness of academic preclinical stud-
ies. In one report, only 11% of published “positive” preclinical cancer studies from
academic labs could be reproduced by the original researchers when working with
The Bottom Line
An early small invivo study can be extremely helpful in demonstrating both
efcacy and preliminary toxicity of the drug. Results can also inform further
rounds of optimization of the compound. During initial animal studies, the
drug should generally be administered using a parenteral route (ip or sc via an
osmotic infusion pump).
3 On theWay totheClinic
106
Amgen scientists. Most academic scientists were unable to reproduce their own
published work, even when working in their own laboratories with their own
reagents, when they were blinded to the experimental groups (Begley and Ellis
2012). In another report, Bayer scientists found that ~75% of published academic
studies could not be replicated, which resulted in termination of efforts to develop
therapeutics based on these academic ndings (Prinz etal. 2011).
These ndings were recently conrmed in a prospective, thorough analysis that
showed only 13% of “positive” preclinical cancer ndings could be reproduced
(Errington etal. 2021a). The irreproducibility of the majority of academic studies,
even when published in “high-prole” journals, has been long known to industry.
As a result, it is routine practice for investors to attempt to independently reproduce
key ndings in another lab prior to any investment. So why is it that published
ndings are of such poor quality?
The following discussion focuses on academic data related to animal studies.
Issues that won’t be discussed include, for example, the importance of using the
right animal models, conrmation using patient specimens, and use of appropriate
study “endpoints.” These issues are all discussed in later sections of this chapter.
Instead, irreproducible studies typically reect a failure to perform studies using
a solid methodology—factors that can be easily remedied by the academic
researchers (Fig.3.2).
3.2.1 Factors that Contribute toIrreproducible Data
3.2.1.1 Failure toFocus onMethods Drives Irreproducible Results
While animal studies can be affected by factors over which researchers may have
little control (as briey discussed below), the principal problem lies with the
inadequate methodology employed by the researchers themselves.
The areas where researchers may have less control include, for example, the
nocturnal nature of rats. Therefore, data related to their immune response, eating,
exercise, ability to learn tasks, etc., may be impacted by the time of day when the
experiment is conducted. Equally, chow feed may be a variable that can affect
Box 3.2: What Surprised an Academic?
In 2004, I temporarily moved from my academic lab to serve as the Chief
Scientic Ofcer (CSO) of KAI Pharmaceuticals. I was hurt when our Chief
Executive Ofcer (CEO), who holds a BA in history, told me, “You will now
learn that your academic work is not as robust as industry’s standard.” Like
you, I take great pride in our work in academia. I felt that conducting blinded
studies, using several species, and reproducing the work in independent labs
all combined to ensure high quality and valid data. That was not enough, I
quickly learned. —DM-R
D. Mochly-Rosen et al.
107
Fig. 3.2 Factors that contribute to irreproducible data in academia and solutions to mitigate these
errors. Abbreviations: ANOVA Analysis of Variance. (Created with BioRender.com)
animal-derived data; some feed is rich in soy and may therefore contribute
feminizing hormones to both males and females; variation in the feed may affect
response to drug uptake and metabolism.
Other potentially confounding factors relate to the housing conditions, including
noise, strong smells, and crowding. Latent or full-blown infection by viruses,
bacteria, mites, and other parasites can also impact study results (see Box 3.3). All
3 On theWay totheClinic
108
these variables should be minimized where possible and detailed information should
be recorded and provided by researchers in any publication or report.
Although these issues can provide challenges when attempting to interpret dis-
cordant results between laboratories, the inability to repeat results in the same labo-
ratory (Begley and Ellis 2012; Errington etal. 2021a) speaks to more fundamental
and important issues including lack of blinding of researchers, lack of randomiza-
tion of animals to the various treatment groups, inadequate cohort sizes, failure to
include positive and negative controls, use of nonvalidated reagents, failure to link
PK exposure and pharmacodynamics (PD), lack of a prespecied power calcula-
tion, and inappropriate analysis of results (Begley 2013; Errington etal. 2021b).
3.2.1.2 Bias andIncomplete Reporting
It is critical that investigators who assess animal data are blinded to the experimen-
tal conditions; unintended bias can greatly affect the analysis, especially when the
endpoint determinants are subjective.
Another bias problem results from dismissing and not reporting negative or
inconsistent data. Although the investigator may have a strong rationale for wanting
to exclude data from certain animals, the reasoning must be described and the
criteria established before the experiment commences and completely described in
the Methods section of the published report. This allows readers to draw their own
conclusions about the legitimacy of the approach. All data (positive and negative)
should be reported, as they may help identify important variables to consider in
human studies. For example, the observation that sex and age can affect the
therapeutic response to drugs in animal models of heart attack was not reported for
a long time. When these ndings were nally reported, reviewers started requesting
that preclinical studies include animals from both sexes.
All studies should include both positive and negative controls. For example,
including a group of animals treated with a drug that was previously approved for
the indication will not only enable a side-by-side comparison of the benet of our
intervention, but will also conrm that the disease model is relevant. This positive
control will serve as the “gold standard” in a head-to-head study and will demonstrate
that the new approach has the potential to be superior (or not) to existing therapies.
Box 3.3: Unsuspected Confounding Factors Can Aect Reproducibility
Lack of reproducibility of preclinical reports does not necessarily mean that
the data are fabricated or wrong (although that may be the case). One of the
better-documented cases of inability to reproduce data in mice relates to the
induction of type I diabetes in non-obese diabetic (NOD) mice. Initial claims
attributed increased diabetes incidence reported by some groups to the differ-
ence in housing the mice in germ-free conditions. However, more recent data
showed that intestinal microbiota are the critical confounding factor; presence
of Bacillus cereus in the gut delayed onset and reduced incidence of type 1
diabetes (King and Sarvetnick 2011).
D. Mochly-Rosen et al.
109
This is referred to as “differentiation” from current therapeutic approaches and is
essential for attracting investment to develop a new therapy.
Academics sometimes assume that certain controls are wasteful. “We have done
these controls before” or “we did not have the funds to include this control again”
are some of the reasons often used. However, control experiments need to be done
side- by- side with the treatment arm, as unexpected factors can contribute to the
outcomes. An investigator in SPARK said that they omitted an oral gavage of their
control subjects before the last blood draw, only to discover later that gavage alone
increases neutrophil number in the blood—possibly due to animal stress.
Unfortunately, the entire study had to be repeated. Concurrent controls are always
essential.
It is important that critical experiments are repeated by a different investigator in
the same lab to ensure that the experimental protocol is detailed enough to be
reproduced by an unbiased researcher. When Dr. Mochly-Rosen rst reported on
the benet of treating animals with an inhibitor of protein kinase C delta after induc-
ing a heart attack, the degree of improvement was so surprising that one skeptic
refused to believe the results. It was good to be able to answer that three members
of the lab had reproduced the same data. It was even better to report that two other
labs had reproduced the data, and it was really a coup when that skeptic obtained the
same data in their own laboratory.
3.2.1.3 Insufficient Statistical Power oftheStudy or Inappropriate
Statistical Analysis
To contain costs, researchers in academia often use too few animals per treatment
cohort. Unfortunately, a p-value smaller than 0.05, although deemed statistically
signicant, is not sufciently robust if the study was performed, for example, with
only 5 animals per treatment group. Rather than contact a statistician prior to
designing a study, most researchers only contact a statistician when attempting to
analyze the data from a suboptimally powered study.
Multiple commentaries urge academics to recognize the critical contribution of
statisticians in preclinical research (e.g., Peers etal. 2012). Statisticians should be
engaged early during study planning to ensure that the number of animals included
is sufcient and that the study is powered to provide an unequivocal answer. This
will not only ease the review process, but importantly will increase the rigor of the
study. If the budget dictates the number of animals per group, researchers risk
generating data that will be uninterpretable.
Biostatisticians can also advise on the appropriateness of the statistical tests used
to analyze the results. Often, there is more than one statistical test available to
compare groups, but characteristics of the data (e.g., size, distribution) may make
some tests inappropriate. For example, we should not use a t-test on nonparamet-
ric data.
3 On theWay totheClinic