Synthetic lethal

CRISPR library 를 이용하여, 또는 이전의 shRNA library를 이요하여 synthetic lethality 를 찾아내는 일이야, 이미 수 많은 논문에서 보여준 흔한 방법론이다. 심지어는 이 실험을 대행해주는 CRO 도 있을 정도. 

PARP inhibitor 의 경우가 좋은 예가 되겠지만, 이 이상으로 market 에서 성공사례가 별로 보이지 않았던 만큼 가능성 대비 효율성이 좋은지는 늘 의문이었다. (논문용이라면 정말 좋은 주제가 아닌가!)

하지만 역시 Cambridge 의 수많은 스타트업들에겐 이 또한 무궁무진한 가능성과 좋은 재료이거늘, Tango 라는 이름의 바이오텍이 $55M 을 획득하였다. 어떤 전략이 숨어있는지 찾아보려고 했으나 홈페이지가 없다. 파이프라인이 궁금하다. 현재 직원이 10명이란다. 

일단 undruggable 한 tumor sup. targeting 전략과 I/O checkpoint inhibitor 들의 efficacy (response rate?) 를 높이는 전략으로 간다니, 일단 screening 에서 볼 phenotype setting 이 어떻게 되는지 궁금하다. (600억에 걸맞게 너무 뻔하지 않기를…) Screening 에서 도출되는 수 많은 false data 를 걸러내는 전략도 궁금하고.

(졸업하고 이런데 취업하면 좋을듯;)

FDA Grants Approval for BAVENCIO® (avelumab), the First Immunotherapy Approved for Metastatic Merkel Cell Carcinoma

Pfizer 가 4번째로 PD-1/PD-L1 약물을 시장에 발매했다. AZ 가 될 줄 알았건만,

링크

ORR 33%, CR 11%, PR 22%

 

이로써, PD-1 inhibitor 2개, PD-L1 inhibitor 2개가 되었고, 기존 약물들과의 경쟁은 어떻게 될지가 궁금하다.

누가 또 아는가, 갑자기 pembrolizumab 이 그랬던 것 처럼, 단숨에 치고 올라올지,,,

기존 Pfizer oncology pipeline 을 보면 어떤 약물과 combo-trial 을 갈지는 당장 감이 오질 않는다. 찾아보기도 귀찮고,,

일단 NSCLC, gastric, ovarian 등 임상 진행 중인 결과가 기존 약물과 직접적인 경쟁이 될 필드이기도 하니 좋은 징조인 것 같다.

하루빨리 durvalumab 도 가세하길…

 

Drug target 을 어떻게 찾지?

 

Drug target 을 어떻게 찾지? 어떤 target 이 좋을까? 라는 상당한 고부가가치의 질문에 ‘바로 이거야’ 라고 선뜻 답할 수 있다면, 내가 여기서 안 이러고 있지. $$$$

어째든간 이래저래 insight 를 길러보야 할 일이다. 본문에도 언급되어 있지만 empirical 한 작업이고, 무수한 trial-and-error 없이는 나올 수 없으니깐, 오늘도 피펫질이나…

계속적으로 자신의 경험을 바탕으로 모델을 만들어가는 일이 중요하다.

 

Keith Robison

First, a quibble.  Diseases: his high bound seems about right, but the reality is that incidences can go down to essentially 1 case.  Particularly in our world of high throughput sequencing, in which Mendelian diseases can be identified with tiny numbers of patients or in which we can parse cancers down to specific combinations of putative causative genetic alterations.  Now at some point rare diseases are just too rare for commercial success, but I’ve had commercial-minded folks at three different companies dodge that question, so I don’t know where that is.  Even if a genetic condition is ultra-rare, finding them is a golden opportunity, as these are natural genetic experiments.  PCSK9 is the poster child for this approach, but I expect there will be many more. 

Let’s look at drug targets from the perspective of making drugs against them.  First we need to decide what is a drug, and that even became quite a mess.  Therapeutics either in use or being considered by reputable groups include energy (X-rays, UV, sound), engineered human cells, engineered bacterial cells, mixtures of natural bacteria, bacteriophages, various flavors of gene therapy, proteins, nucleic acids, small organic molecules and even inorganic ions and gases.  This essay will mostly focus on small molecules with perhaps some touching on some of those others.

In any disease, there are a set of biological/biochemical phenomena which represent a deviation from the biological norm and we wish to correct.  A goal of modern biology is to parse these down to lots of subphenomena, chains of events which constitute the disease.  Sometimes these may even be simple, but it is possible to make complex the simple. 

For example, there are a growing number of single-gene Mendelian disorders.  Some of these can be treated with simple dietary interventions (e.g. phenylketonuria — and by simple I mean in concept; it is well known that actually following a PKU diet requires constant vigilance in the style of Alastor Moody).  Genzyme made a mint selling an enzyme replacement therapy for Gaucher’s disease.  These direct assaults on the disease are appealing, but perhaps not always possible  An alternative would be to somehow use a drug to ameliorate the downstream side effects of the imbalance or prevent the imbalance. 

Not a metabolic error, but an example would be sickle cell anemia, which is caused by a defective beta hemoglobin.  There’s growing hope that this can be cured with gene therapy approaches, but some existing treatments actually work by stimulating the body to use a different form of hemoglobin, the fetal hemoglobin used prior to being born. 

As another example, I’ve spent a sizable amount of my professional career thinking about attacking cancer via understanding what goes on in a cancer cell.  What are the critical genetic circuits that make a cancer cell a cancer cell?  I also spent time at Infinity on a program, which sadly failed, targeting the tissue around a cancer cell, the stroma.  There is a great deal of evidence that the tissue around a tumor can interfere with treatment.  There’s also the whole approach of targeting angiogenesis, which hasn’t proven to be a panacea for cancer but remains an important topic.

Actually, more than a little time at Millennium was spent trying to understand how much of bortezomib’s therapeutic effect in myeloma is due to effects on the myeloma cells themselves and how much is from altering their deranged interactions with other members of the bone marrow community.  For example, myeloma is sometimes diagnosed due to a bone fracture because myeloma cells can alter the balance in bone between the bone-building osteoblasts and the bone-destroying osteoclasts.  The latter are my favorite bit of Greek root onomatopoeia, from clastos “to break”.  Can’t you hear a pot shattering with that word?  Anyway, the usual balance between these is responsible for the great strength of bone and the ability to heal, but myeloma cells drive excess clastos

Now there’s a huge resurgence of effort in the immune angle on cancer.  How can we stimulate and/or train immune cells to go after tumors?  Conversely, when immune cells are co-opted by tumors, which can also happen, how do we alter that?  Trying to stray back to my point, we can tackle tumors by going after the tumor, going after the cells near the tumor or targeting the immune response to cancer.   Similar ideas hold for many other disease areas: you can target the diseased tissue or some other part of the body.  How should we target diseases? With apologies to Malcolm X, “By any means necessary”.

Thinking about this reminded me of my deficiencies in higher level physiology.  When my parents throw medical questions at me one of my standard joke defenses is that I didn’t study much at levels higher than a cell.  Genes beget RNAs that often beget proteins which change cells and biochemistry, but too often I stop there (doesn’t help that I work on bacteria, in which there isn’t a lot above that and what exists isn’t well understood).  Cells are in tissues, tissues in organs, organs in systems and ultimately everything comes together to function as a human body.  Thinking about drugs ultimately requires thinking about all those levels.

Okay, so what’s a target?  Well, most targets fall in a few categories: lipid, DNA, RNA and protein.

Lipids haven’t been a hyperproductive target, but there are ionophore antibiotics you can buy over-the-counter in the U.S., so we shouldn’t discount them altogether.  Some anesthetics may work by simply altering membrane properties, or at least that was a hypothesis that has existed in the past. But it is difficult to get specificity against lipids and membranes this way, so not an active area.

Some drugs just damage DNA directly.  Primarily anticancer drugs — nasty alkylating agents and such.  Again, hard to get specificity.

RNA isn’t the target of many drugs, but perhaps that will change in the future.  Certain antibiotics, particularly the aminoglycosides (e.g. streptomycin) do specifically target RNA.  Serial bioentrepeneur Michael Gilman’s newest company is trying to target RNA as well, though his effort as well as antisense and siRNA approaches are typically aimed at changing proteins.  The biology of interest is happening at the protein level, as opposed to aminoglycosides that really are targeting functional RNA (ribosomal RNA).

So then proteins.  They are the bulk of targets and probably will be for the forseeable future.  And then the big problem is druggability, which I wrote on a while back.  To summarize, we can identify human proteins which are relevant to disease but many of these are difficult-to-impossible to reach with drugs.  That’s why approaches like drugging RNA and siRNA are appealing or others which are emerging: to drug the undruggable. 

To give an idea of the problem, we’ve known for something like 30 years now that a huge number of solid tumors, and also some hematologic ones, either have mutations in KRAS or NRAS (or rarely HRAS) or are signalling through these proteins.  They are small GTPases with many protein interactions; the extremely slow GTPase activity serves as a molecular timer and some interactions make the timer run faster or slower, whereas others are the downstream acceptors of the timing signal.  But KRAS has essentially defied drug discovery, because it is an intracellular protein with few crags on its surface. My employer is one of the groups (something that wasn’t disclosed when I wrote the original piece) trying to drug KRAS despite these challenges. Of the flood of possible disease targets that have emerged in the last few decades, KRAS is more then norm than the exception.

If you want to hit an extracellular target, then antibodies are an option, unless your target is behind the blood-brain barrier.  For intracellular targets, small molecules rule — with those exceptions.  For a small molecule to work, the general rule is it must bind specifically near the active site of the protein.  Or it needs to find a nice pocket somewhere that allosterically alters the protein, perhaps changing the shape or locking in one specific structural pose.

The end result of this is that most small molecule drugs target a very small number of categories of proteins.  Enzymes, but not all enzymes.  Cell surface receptors, particularly the G-protein coupled receptor (GPCR) type, but again not all receptor types.  Transporters and ion channels in the membrane.  Nuclear hormone receptors, which are receptors but not cell-surface ones.  There’s all sorts of exceptions (which I can’t think of) and I think there are a few small molecule drugs with unknown or controversial mechanisms (lithium isn’t settled yet, is it?).  

Then there is the problem of specificity.  If I give a drug to a patient targeting GPCR X, how many other GPCRs will I hit?  What side effects will that cause?  Or perhaps GPCR X mediates the pathology of interest but also some perfectly normal physiology elsewhere?  The drug may hit some totally unrelated protein as well, particularly after the liver and its enzymes have given it a going over.  Perhaps the drug accumulates in some strange way that enables it to hit a low-affinity target in an undesirable location.  Or pH or other local conditions alter the drug’s action in some interesting way.

Plus, you can’t get a small molecule to do just anything.  With enzymes, your molecule is almost certainly going to be an inhibitor.  So if you want to stimulate an enzyme, that enzyme probably won’t be a direct target — you’ll probably use “the enemy of my friend is my enemy” thinking to find something whose inhibition leads to stimulation of the enzyme.  Channels and receptors are more complex things, sometimes you can find agonists which activate them and other times you can find antagonists which truly block them or inverse agonists that bias them away from being active.

As noted, there are many efforts to break out of the frustration of druggability, but all are early.  Our approach is to reprogram some nifty natural product chemistry.  Three natural product drugs which break the usual rules of druggabilty by binding to flat molecular surfaces are rapamycin, FK506 and cyclosporin. Each does this by “cheating”; rather than tackling a target alone they bind an abundant intracellular protein with one end of the molecule and then that hybrid molecule binds to the flat surface on the target with amazing specificity. 

Another approach with some conceptual similarity are PROTACs, which is a strategy by which small molecules are used to bring together a target protein and a cellular enzyme that gives proteins a kiss-of-death.  Apply a drug of this type and the targeted protein should disappear from the cell in a dose-dependent manner.

Vertex’s first cystic fibrosis drug pulls off a minor miracle: a drug which actually improves the function of a flawed protein.  I was one of the fools who thought this approach was doomed to failure when Vertex first announced the effort; it is great to be proven wrong.  This idea of “chemical chaperones” appears to be gaining some momentum, but how many disease targets could it apply to? 
There’s probably a bunch out there I haven’t seen or lost track of.  Cell penetrating peptides and ways of getting antibodies into cells have been discussed, but I don’t know how they stand.  Local unicorn Moderna wants to express functional mRNAs as drugs.  As with any nucleic acid scheme, they will have to deal with problems of delivery (liver or direct injection are great, anywhere else appears to be a challenge) and toxicity (both Moderna and Alnylam have had recent news around toxicity for their RNA-based drugs).

So suppose you have a novel target and you’ve found a candidate drug against it.  You’re far from done.  The problem is we really don’t understand the biology at anywhere near the level you’d need to predict outcomes. Until you’ve drugged patients, you don’t have a real chance of understanding the biology of drugging patients, and even then you don’t know every possible drug/diet/environment/genetic interaction that might show up in a few patients and wreck havoc.  Laboratory approaches to validating targets give insight, whether they are cell culture models or RNAi experiments or overexpression experiments or what have you, but they aren’t the same as drugging a target.  Nor are those natural genetic models I praised near the beginning of this piece; partial inhibition of a target chemically with defined temporal endpoints is still different than a complete genetic knockout which has been lived with since conception.

How so?  Well, for one thing cells are resilient.  Particularly cancer cells, but it applies everywhere.  There may be some less efficient or backup pathway that can be activated.  Or some sort of rescue-and-repair pathway.  Or perhaps your choice of blocking the pathway doesn’t pan out because too many signals enter downstream of your target.  Perhaps the dynamics of the target’s activity play a complex role.  Biology is complex and our ignorance runs deep.

Another, touched on above, are undesired effects of your drug.  Hitting related targets.  Hitting the same target in cells and tissues you don’t wish to target.  Hitting completely unrelated targets.  There could also be some previously uncharted pathway that depends on your target and now isn’t happy either.  Or you’ve inhibited an enzyme, and now the precursor it works on backs up in the cell, triggering an undesired response.  To give one notorious example similar to this, creating a fat-like substance (Olestra) for making potato chips which can’t be absorbed in the gut means you’ve introduced a very good lubricant to the lower portion of the gut.  ‘Nuff said.

Of course, even getting to targets can be a problem.  Look at the wasteland which is Alzheimer’s disease approaches; it’s easy to conclude that little of what we think we know about the biology of this awful disease is of any value.  You can moderate your risk on a specific target using a cell-based screen, but now you’ve traded one kind of risk (target) for another type (model).  Again, for something like Alzheimer’s there may simply not be any trusted models for a phenotypic screen.

I have hardly scratched the surface of what can go wrong in drug development program.  For example, animal models can give grossly misleading results.  There’s plenty more; since I don’t specifically focus on these I let the people who do keep the catalog of what can go awry in their sector.  Sometimes it seems remarkable that any drug has ever been successfully launched and made a difference, but of course many have.  Remembering that — and all the patients who still have pressing needs for new drugs — is sometimes critical to keeping the spirit to continue plunging ever forward. 

원문 링크

Atezolizumab vs docetaxel in patients with previously treated NSCLC (OAK): a phase 3, open-label, multicentre randomised controlled trial

Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomised controlled trial.

Achim Rittmeyer, Fabrice Barlesi, Daniel Waterkamp, Keunchil Park, Fortunato Ciardiello, Joachim von Pawel, Shirish M Gadgeel, Toyoaki Hida, Dariusz M Kowalski, Manuel Cobo Dols, Diego L Cortinovis, Joseph Leach, Jonathan Polikoff, Carlos Barrios, Fairooz Kabbinavar, Osvaldo Arén Frontera, Filippo De Marinis, Hande Turna, Jong-Seok Lee, Marcus Ballinger, Marcin Kowanetz, Pei He, Daniel S Chen, Alan Sandler, David R Gandara, and OAK Study Group.

BACKGROUND:Atezolizumab is a humanised antiprogrammed death-ligand 1 (PD-L1) monoclonal antibody that inhibits PD-L1 and programmed death-1 (PD-1) and PD-L1 and B7-1 interactions, reinvigorating anticancer immunity. We assessed its efficacy and safety versus docetaxel in previously treated patients with non-small-cell lung cancer.

METHODS:We did a randomised, open-label, phase 3 trial (OAK) in 194 academic or community oncology centres in 31 countries. We enrolled patients who had squamous or non-squamous non-small-cell lung cancer, were 18 years or older, had measurable disease per Response Evaluation Criteria in Solid Tumors, and had an Eastern Cooperative Oncology Group performance status of 0 or 1. Patients had received one to two previous cytotoxic chemotherapy regimens (one or more platinum based combination therapies) for stage IIIB or IV non-small-cell lung cancer. Patients with a history of autoimmune disease and those who had received previous treatments with docetaxel, CD137 agonists, anti-CTLA4, or therapies targeting the PD-L1 and PD-1 pathway were excluded. Patients were randomly assigned (1:1) to intravenously receive either atezolizumab 1200 mg or docetaxel 75 mg/m(2) every 3 weeks by permuted block randomisation (block size of eight) via an interactive voice or web response system. Coprimary endpoints were overall survival in the intention-to-treat (ITT) and PD-L1-expression population TC1/2/3 or IC1/2/3 (≥1% PD-L1 on tumour cells or tumour-infiltrating immune cells). The primary efficacy analysis was done in the first 850 of 1225 enrolled patients. This study is registered with ClinicalTrials.gov, number NCT02008227.

FINDINGS:Between March 11, 2014, and April 29, 2015, 1225 patients were recruited. In the primary population, 425 patients were randomly assigned to receive atezolizumab and 425 patients were assigned to receive docetaxel. Overall survival was significantly longer with atezolizumab in the ITT and PD-L1-expression populations. In the ITT population, overall survival was improved with atezolizumab compared with docetaxel (median overall survival was 13·8 months [95% CI 11·8-15·7] vs 9·6 months [8·6-11·2]; hazard ratio [HR] 0·73 [95% CI 0·62-0·87], p=0·0003). Overall survival in the TC1/2/3 or IC1/2/3 population was improved with atezolizumab (n=241) compared with docetaxel (n=222; median overall survival was 15·7 months [95% CI 12·6-18·0] with atezolizumab vs 10·3 months [8·8-12·0] with docetaxel; HR 0·74 [95% CI 0·58-0·93]; p=0·0102). Patients in the PD-L1 low or undetectable subgroup (TC0 and IC0) also had improved survival with atezolizumab (median overall survival 12·6 months vs 8·9 months; HR 0·75 [95% CI 0·59-0·96]). Overall survival improvement was similar in patients with squamous (HR 0·73 [95% CI 0·54-0·98]; n=112 in the atezolizumab group and n=110 in the docetaxel group) or non-squamous (0·73 [0·60-0·89]; n=313 and n=315) histology. Fewer patients had treatment-related grade 3 or 4 adverse events with atezolizumab (90 [15%] of 609 patients) versus docetaxel (247 [43%] of 578 patients). One treatment-related death from a respiratory tract infection was reported in the docetaxel group.

INTERPRETATION:To our knowledge, OAK is the first randomised phase 3 study to report results of a PD-L1-targeted therapy, with atezolizumab treatment resulting in a clinically relevant improvement of overall survival versus docetaxel in previously treated non-small-cell lung cancer, regardless of PD-L1 expression or histology, with a favourable safety profile.

FUNDING:F. Hoffmann-La Roche Ltd, Genentech, Inc.

Lancet, 2017 vol. 389 (10066) pp. 255-265.

http://linkinghub.elsevier.com/retrieve/pii/S014067361632517X

스크린샷 2017-03-23 오전 12.45.04

스크린샷 2017-03-23 오전 12.45.32

스크린샷 2017-03-23 오전 12.46.33TC1/2/3 or IC1/2/3: PD-L1 >= 1%
TC2/3 or IC2/3: 5%
TC3: 50% Tumor PD-L1
IC3: 10% Immune cell PD-L1
TC0 and IC0: PD-L1 < 1%

All images are from 여기.

Perjeta + Herceptin (APHINITY P3)

Roche 는 정말 약도 잘 만들지만, 그 보다도 약 파는 능력은 가히 둘째가라면 서러워하는 (second to none) 회사 인 듯.

Perjeta (Pertuzumab) 은 Herceptin (Trastuzumab) 과 동일하게 her2 에 결합해 signaling 을 저해 하는데, 일단은 결합 부위가 다르다.
스크린샷 2017-03-14 오후 10.36.01.png

APHINITY clinical trial 에 대해서는, ASCOpost 를 참고.

(Adjuvant Pertuzumab and Herceptin IN Initial TherapY in Breast Cancer) 

APHINITY (NCT01358877/BO25126/BIG 4-11) is an international, phase III, randomized, double-blind, placebo-controlled, two-arm study evaluating the efficacy and safety of pertuzumab plus trastuzumab and chemotherapy compared to trastuzumab and chemotherapy as an adjuvant therapy in 4,805 people with operable HER2-positive early breast cancer.

People enrolled in the study underwent surgery and were randomized to one of two arms (1:1) to receive either:

  • Six to eight cycles of chemotherapy (anthracycline-containing or non–anthracycline-containing regimen) with pertuzumab and trastuzumab, followed by pertuzumab and trastuzumab every 3 weeks for a total of 1 year of treatment.
  • Six to eight cycles of chemotherapy (anthracycline-containing or non–anthracycline-containing regimen) with placebo and trastuzumab, followed by placebo and trastuzumab every 3 weeks for a total of 1 year of treatment. 

Radiotherapy and/or endocrine therapy could be initiated at the end of adjuvant chemotherapy. The APHINITY study allowed for a range of standard chemotherapy regimens to be used and both lymph node–positive and lymph node–negative participants were eligible for enrollment. The primary efficacy endpoint of the APHINITY study is invasive disease–free survival. Secondary endpoints include cardiac and overall safety, overall survival, disease-free survival, and health-related quality of life.