Rev 419: Fill & Download for Free

PA and NJ have a reciprocal income tax agreement, so you do not owe PA income tax and should file Form REV-419 (Page on state.pa.us ) with your employer to have them withhold NJ income tax.

A.: Yes.String theory is a layer-cake of quantum field theory models. At its underlying, most fundamental level, there is the “worldsheet” quantum field theory, wherein Daniel Friedan discovered in 1979 [1] that such models are stable under quantum fluctuations (→ renormalization, beta function) if the metric in the “target space” (probed by those worldsheet quantum fields) satisfies the Einstein's field equations — to leading order in a well-defined perturbative computation. That is, the quantum field theory intrinsic to the strings themselves is stable precisely if the strings propagate through a spacetime governed by general relativity — to leading order in [math]\alpha'[/math].Thereby, this “worldsheet” quantum field theory intrinsic to the strings themselves indeed “predicts” (“post-dicts,” 60 years later) general relativity — to leading order in [math]\alpha'[/math], and prescribes also its corrections (at successively higher order in [math]\alpha'[/math])!More reasonably, this says that general relativity is inevitable in string theory.[1] D. Friedan: “Nonlinear Sigma Models in 2+ε Dimensions,”​ Phys. Rev. Lett. 45 (1980) 1057; expanded in “Nonlinear models in 2+ε dimensions,” Ann. Phys. 163 (1985) 318– 419.See also my answer to “What is the relationship between quantum field theory and string theory?”

Most drug candidates these days do fail at a late stage of the drug development process.In 2011, Pammolli et al examined a large global database of R&D projects on >28000 compounds investigated from 1990 to 2004 (1). They found a clear trend of increasing attrition rates across all stages of the drug development process including an increase to >50% at Phase III by 2004 (see below from 1).In 2012, Mestre-Ferrandiz et al performed a meta-analysis of 11 studies from 1979 to 2011 that analyzed the A-to-Z process for new drugs (2). They estimated probability of Phase III success ranges from 50 to 71%, i.e., Phase III failures ranging from 29 to 50%.In 2013, a group of Boston Consulting Group analysts examined the 2002 to 2011 record of 842 individual molecules with known full development outcomes. They found 205, i.e., 24.3%, gained regulatory approval while the remaining 637, i.e., 75.7%, failed in Phase II or later (3). Their analysis cuts right across the BioPharma landscape, covering small (<$200 million/year on R&D), medium ($200 million to 1 billion) and large (>$1 billion) companies, both private and public, and located not just in the US but also in the EU and the rest of the world. In their analysis, the factors that correlated most with drug development success were (see below from 3).Scientific judgment or 'acumen' in terms of shutting projects down early, rather than late.Solid track record of the company's R&D in terms of publications and patents per US dollar of R&D expenditure, and citation records (# of times papers were cited by others).A 2012 study by Pfizer also found scientific judgment / 'acumen' to be the most critical quality. Their analysis found that 2/3rd of the company's Phase I candidates, 'assets', were advanced down the pipeline for further development even when data was already available that they would likely fail (4).A 2014 AstraZeneca analysis of the decision-making about their drug pipeline concluded that the 'right culture' was crucial for effective decision-making, i.e., which candidates to advance and which to terminate early (5).Thus, several studies have now not only pointed out increasing late stage failures for new drugs but also suggest part of the problem is too many poor drug candidates are advancing to later stages, i.e., failing late rather than early. It also takes ever longer for a drug candidate to advance down the pipeline, typically ~48 months and ~US $42 million to get from selection to Phase II (2, 6). Yet barring a rare report here and there like Eli Lilly and Company's Chorus (7), which claims to have shaved this time down to ~26 to 28 months, nothing much seems to change about the drug development process. Why? Obviously, can't be a simple case of one or two or even three simple factors. Rather, this status quo is the sum of entrenched cultural, technical and commercial imperatives.Across the board, regardless of company size or therapeutic area, failing late rather than early suggests drug development decision-making appears mired in its version of the Concorde effect, i.e., Sunk costs. Why? As with just about everything in life, outcomes depend on the kinds of behaviors that are rewarded. Here, perverse incentives are one likely explanation. Currently, success for biopharma R&D scientific teams tracks with advancement of their drug candidate, i.e., 'progression-seeking', and not scientific 'truth-seeking' (3). Team members' personal success, promotion, bonuses, organizational influence, these and more depend on their advancing their 'asset' through the drug development pipeline, not on even-handedly examining both the positive and negative scientific data about it. When such a culture prevails, no surprise a drug candidate, aka the 'asset', is more akin to the proverbial hot potato, shunted down the development pipeline to become someone else's responsibility. This is one possibility.Another major reason could be the pervasive culture of relentless focus on short-term profits. After all, markets react exuberantly every time a drug candidate moves forward in the development pipeline. In turn, company management and shareholders become extremely attuned, even habituated to such reactions. After all these reflect very well on their immediate bottom-lines.Having the 'right culture' then means something much more consequential than merely rewarding scientific 'truth-seeking'. It means having management with sufficiently strong backbone to eschew short-term profit in favor of long-term success predicated on scientifically extremely thoroughly vetted drug candidates. That entails more investment (money, personnel, resources and time) on the earlier stages of drug development to sort through a bunch of candidates more thoroughly and failing them expeditiously so that a higher proportion of sure bets progress to the next stage.However, reality shows though that short-term profits remain the major focus for all involved, the scientific teams, the management, the shareholders, the market, usually all the way until the inevitable chickens, i.e., putting off difficult decisions until years down the road, come home to roost in the form of Phase III failures.Other reasons that feed into lengthy development times and increasing late-stage failures area) The notion that treatments for simpler diseases have already been achieved in the past and remaining challenges are much more scientifically difficult. Such challenges include lack of appropriate model systems, of in vitro approaches, of biomarkers, etc., and not knowing or understanding well enough the right patient/tissue/target.b) Regulatory landscape is increasingly more risk-averse and there's a much higher, perhaps even implausible, bar for safety.c) Comprehensive health economics weren't part of a given drug candidate's vetting early in the process. Years down the road, when they're finally prioritized, the data suggest insufficient market or vexing reimbursement and/or pricing issues or cost versus benefit analysis renders product inferior to standard of care.Bibliography1. Pammolli, Fabio, Laura Magazzini, and Massimo Riccaboni. "The productivity crisis in pharmaceutical R&D." Nature reviews Drug discovery 10.6 (2011): 428-438. http://moglen.law.columbia.edu/twiki/pub/LawNetSoc/BahradSokhansanjFirstPaper/10NatRevDrugDisc428_pharma_productivity_crisis_2011.pdf2. Mestre-Ferrandiz, Jorge, Jon Sussex, and Adrian Towse. "The R&D cost of a new medicine." London: Office of Health Economics (2012). https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwj3-Jye-d_NAhWLXh4KHSYqBSwQFggcMAA&url=https%3A%2F%2Fwww.ohe.org%2Fsystem%2Ffiles%2Fprivate%2Fpublications%2F380%2520-%2520R%2526D%2520Cost%2520NME%2520Mestre-Ferrandiz%25202012.pdf%3Fdownload%3D1&usg=AFQjCNGsMRSRPXQG_quSDMqggHRTYKg9VA&bvm=bv.126130881,d.dmo3. Ringel, Michael, et al. "Does size matter in R&D productivity? If not, what does?." Nature Reviews Drug Discovery 12.12 (2013): 901-902. http://media-publications.bcg.com/NRDD-Ringel-Dec2013.pdf4. Morgan, Paul, et al. "Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival." Drug discovery today 17.9 (2012): 419-424.5. Cook, David, et al. "Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework." Nat Rev Drug Discov 13.6 (2014): 419-431. http://admin.indiaenvironmentportal.org.in/files/file/AstraZeneca%E2%80%99s%20drug%20pipeline.pdf6. Adams, Christopher Paul, and Van Vu Brantner. "Spending on new drug development1." Health economics 19.2 (2010): 130-141. https://www.researchgate.net/profile/Christopher_Adams/publication/228247913_Spending_on_New_Drug_Development/links/55afad7208aeb9239916d99d.pdf7. Owens, Paul K., et al. "A decade of innovation in pharmaceutical R&D: the Chorus model." Nature Reviews Drug Discovery 14.1 (2015): 17-28.Thanks for the R2A, Anonymous.